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1
Yuan, Wei, Jin Wang, and Wenbo Xu. "Shift Pooling PSPNet: Rethinking PSPNet for Building Extraction in Remote Sensing Images from Entire Local Feature Pooling." Remote Sensing 14, no. 19 (2022): 4889. http://dx.doi.org/10.3390/rs14194889.
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Building extraction by deep learning from remote sensing images is currently a research hotspot. PSPNet is one of the classic semantic segmentation models and is currently adopted by many applications. Moreover, PSPNet can use not only CNN-based networks but also transformer-based networks as backbones; therefore, PSPNet also has high value in the transformer era. The core of PSPNet is the pyramid pooling module, which gives PSPNet the ability to capture the local features of different scales. However, the pyramid pooling module also has obvious shortcomings. The grid is fixed, and the pixels close to the edge of the grid cannot obtain the entire local features. To address this issue, an improved PSPNet network architecture named shift pooling PSPNet is proposed, which uses a module called shift pyramid pooling to replace the original pyramid pooling module, so that the pixels at the edge of the grid can also obtain the entire local features. Shift pooling is not only useful for PSPNet but also in any network that uses a fixed grid for downsampling to increase the receptive field and save computing, such as ResNet. A dense connection was adopted in decoding, and upsampling was gradually carried out. With two open datasets, the improved PSPNet, PSPNet, and some classic image segmentation models were used for comparative experiments. The results show that our method is the best according to the evaluation metrics, and the predicted image is closer to the label.
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Li, Yuxia, Peng Li, Hailing Wang, Xiaomei Gong, and Zhijun Fang. "CAML-PSPNet: A Medical Image Segmentation Network Based on Coordinate Attention and a Mixed Loss Function." Sensors 25, no. 4 (2025): 1117. https://doi.org/10.3390/s25041117.
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The problems of missed segmentation with fuzzy boundaries of segmented regions and small regions are common in segmentation tasks, and greatly decrease the accuracy of clinicians’ diagnosis. For this, a new network based on PSPNet, using a coordinate attention mechanism and a mixed loss function for segmentation (CAML-PSPNet), is proposed. Firstly, the coordinate attention module splits the input feature map into horizontal and vertical directions to locate the edge position of the segmentation target. Then, a Mixed Loss function (MLF) is introduced in the model training stage to solve the problem of the low accuracy of small-target tumor segmentation. Finally, the lightweight MobilenetV2 is utilized in backbone feature extraction, which largely reduces the model’s parameter count and enhances computation speed. Three datasets—PrivateLT, Kvasir-SEG and ISIC 2017—are selected for the experimental part, and the experimental results demonstrate significant enhancements in both visual effects and evaluation metrics for the segmentation achieved by CAML-PSPNet. Compared with Deeplabv3, HrNet, U-Net and PSPNet networks, the average intersection rates of CAML-PSPNet are increased by 2.84%, 3.1%, 5.4% and 3.08% on lung cancer data, 7.54%, 3.1%, 5.91% and 8.78% on Kvasir-SEG data, and 1.97%, 0.71%, 3.83% and 0.78% on ISIC 2017 data, respectively. When compared to other methods, CAML-PSPNet has the greatest similarity with the gold standard in boundary segmentation, and effectively enhances the segmentation accuracy for small targets.
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McCall, Hugh, and Heather Hadjistavropoulos. "Online Therapy to Treat Mental Health Concerns Among Police and Other Public Safety Personnel." Applied Police Briefings 1, no. 2 (2024): 25–27. http://dx.doi.org/10.22215/apb.v1i2.5011.
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PSPNET is a research unit at the University of Regina in Saskatchewan, Canada, that provides free online therapy programs to Canadian first responders and other public safety personnel (PSP). The first 560 clients (33% police) in PSPNET’s most popular therapy program—an internet-delivered cognitive behavioural therapy (ICBT) program called the PSP Wellbeing Course—have shown large improvements in mental health, as well as good satisfaction and engagement with the program. Treatment outcomes were similar for different groups of clients (e.g., men and women, clients in different occupational groups), suggesting that PSPNET’s online therapy program is effective for various groups of PSP.
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Zhao, Jinling, Zheng Li, Yu Lei, and Linsheng Huang. "Application of UAV RGB Images and Improved PSPNet Network to the Identification of Wheat Lodging Areas." Agronomy 13, no. 5 (2023): 1309. http://dx.doi.org/10.3390/agronomy13051309.
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As one of the main disasters that limit the formation of wheat yield and affect the quality of wheat, lodging poses a great threat to safety production. Therefore, an improved PSPNet (Pyramid Scene Parsing Network) integrating the Normalization-based Attention Module (NAM) (NAM-PSPNet) was applied to the high-definition UAV RGB images of wheat lodging areas at the grain-filling stage and maturity stage with the height of 20 m and 40 m. First, based on the PSPNet network, the lightweight neural network MobileNetV2 was used to replace ResNet as the feature extraction backbone network. The deep separable convolution was used to replace the standard convolution to reduce the amount of model parameters and calculations and then improve the extraction speed. Secondly, the pyramid pool structure of multi-dimensional feature fusion was constructed to obtain more detailed features of UAV images and improve accuracy. Then, the extracted feature map was processed by the NAM to identify the less significant features and compress the model to reduce the calculation. The U-Net, SegNet and DeepLabv3+ were selected as the comparison models. The results show that the extraction effect at the height of 20 m and the maturity stage is the best. For the NAM-PSPNet, the MPA (Mean Pixel Accuracy), MIoU (Mean Intersection over Union), Precision, Accuracy and Recall is, respectively, 89.32%, 89.32%, 94.95%, 94.30% and 95.43% which are significantly better than the comparison models. It is concluded that NAM-PSPNet has better extraction performance for wheat lodging areas which can provide the decisionmaking basis for severity estimation, yield loss assessment, agricultural operation, etc.
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Yang, Chengzhi, and Hongjun Guo. "A Method of Image Semantic Segmentation Based on PSPNet." Mathematical Problems in Engineering 2022 (August 9, 2022): 1–9. http://dx.doi.org/10.1155/2022/8958154.
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Image semantic segmentation is a visual scene understanding task. The goal is to predict the category label of each pixel in the input image, so as to achieve object segmentation at the pixel level. Semantic segmentation is widely used in automatic driving, robotics, medical image analysis, video surveillance, and other fields. Therefore, improving the effect and accuracy of image semantic segmentation has important theoretical research significance and practical application value. This paper mainly introduces the pyramid scene parsing network PSPNet based on pyramid pooling and proposes a parameter optimization method based on PSPNet model using GPU distributed computing method. Finally, it is compared with other models in the field of semantic segmentation. The experimental results show that the accuracy of the improved PSPNet model in this paper has been significantly improved on Pascal VOC 2012 + 2017 data set.
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Gao, Jianqin, and Kaihua Cui. "Coal Image Recognition Method Based on Improved Semantic Segmentation Model of PSPNET Network." Modern Applied Science 17, no. 2 (2023): 1. http://dx.doi.org/10.5539/mas.v17n2p1.
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To implement the intelligence and automation of coal mines, coal recognition plays a crucial role. In order to further improve the accuracy and speed of intelligent coal recognition, this paper proposes a semantic segmentation model based on an improved PSPNET network. (1) The lightweight MobilenetV2 module is used as the backbone feature extraction network. Compared to traditional networks, it has fewer parameters while achieving higher recognition accuracy and speed.(2) The Convolutional Block Attention Module (CBAM) is introduced into the Pyramid Pooling Module (PPM) to enhance the network's ability to extract detailed features and effectively fuse spatial and channel information, thus improving the segmentation accuracy of the model.(3) Data augmentation and image feature enhancement methods are employed to overcome sample distribution differences, enhance model generalization, and adapt to coal-rock recognition tasks in different application scenarios. The proposed approach is tested on a self-made coal segmentation dataset and compared with the unimproved PSPNET, Hernet, U-net, and DeeplabV3+ models in terms of Mean Intersection over Union (Miou), recognition accuracy, edge detail recognition, model size, and parameter count. Experimental results demonstrate that compared to other models, the improved PSPNET network not only has lower computational complexity and parameter count but also exhibits stronger coal detail feature extraction capability, higher segmentation accuracy, and better processing efficiency.Finally, the improved PSPNET model was trained and tested on a coal rock image segmentation dataset with image feature enhancement.The accuracy, MIU and MPA of the improved PSPNET network reached 65.04, 73.15 and 74.27 respectively.It can be seen that the improved network has superior feature extraction ability and computational efficiency to achieve coal surface image recognition. This verifies the feasibility and effectiveness of the proposed method in the actual coal rock image recognition task.
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Gumus, Kazim Z., Julien Nicolas, Dheeraj R. Gopireddy, Jose Dolz, Seyed Behzad Jazayeri, and Mark Bandyk. "Deep Learning Algorithms for Bladder Cancer Segmentation on Multi-Parametric MRI." Cancers 16, no. 13 (2024): 2348. http://dx.doi.org/10.3390/cancers16132348.
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Background: Bladder cancer (BC) segmentation on MRI images is the first step to determining the presence of muscular invasion. This study aimed to assess the tumor segmentation performance of three deep learning (DL) models on multi-parametric MRI (mp-MRI) images. Methods: We studied 53 patients with bladder cancer. Bladder tumors were segmented on each slice of T2-weighted (T2WI), diffusion-weighted imaging/apparent diffusion coefficient (DWI/ADC), and T1-weighted contrast-enhanced (T1WI) images acquired at a 3Tesla MRI scanner. We trained Unet, MAnet, and PSPnet using three loss functions: cross-entropy (CE), dice similarity coefficient loss (DSC), and focal loss (FL). We evaluated the model performances using DSC, Hausdorff distance (HD), and expected calibration error (ECE). Results: The MAnet algorithm with the CE+DSC loss function gave the highest DSC values on the ADC, T2WI, and T1WI images. PSPnet with CE+DSC obtained the smallest HDs on the ADC, T2WI, and T1WI images. The segmentation accuracy overall was better on the ADC and T1WI than on the T2WI. The ECEs were the smallest for PSPnet with FL on the ADC images, while they were the smallest for MAnet with CE+DSC on the T2WI and T1WI. Conclusions: Compared to Unet, MAnet and PSPnet with a hybrid CE+DSC loss function displayed better performances in BC segmentation depending on the choice of the evaluation metric.
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Qiao, Yichen, Yaohua Hu, Zhouzhou Zheng, et al. "A Diameter Measurement Method of Red Jujubes Trunk Based on Improved PSPNet." Agriculture 12, no. 8 (2022): 1140. http://dx.doi.org/10.3390/agriculture12081140.
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A trunk segmentation and a diameter measurement of red jujubes are important steps in harvesting red jujubes using vibration harvesting robots as the results directly affect the effectiveness of the harvesting. A trunk segmentation algorithm of red jujubes, based on improved Pyramid Scene Parsing Network (PSPNet), and a diameter measurement algorithm to realize the segmentation and diameter measurement of the trunk are proposed in this research. To this end, MobilenetV2 was selected as the backbone of PSPNet so that it could be adapted to embedded mobile applications. Meanwhile, the Convolutional Block Attention Module (CBAM) was embedded in the MobilenetV2 to enhance the feature extraction capability of the model. Furthermore, the Refinement Residual Blocks (RRBs) were introduced into the main branch and side branch of PSPNet to enhance the segmentation result. An algorithm to measure trunk diameter was proposed, which used the segmentation results to determine the trunk outline and the normal of the centerline. The Euclidean distance of the intersection point of the normal with the trunk profile was obtained and its average value was regarded as the final trunk diameter. Compared with the original PSPNet, the Intersection-over-Union (IoU) value, PA value and Fps of the improved model increased by 0.67%, 1.95% and 1.13, respectively, and the number of parameters was 5.00% of that of the original model. Compared with other segmentation networks, the improved model had fewer parameters and better segmentation results. Compared with the original network, the trunk diameter measurement algorithm proposed in this research reduced the average absolute error and the average relative error by 3.75 mm and 9.92%, respectively, and improved the average measurement accuracy by 9.92%. To sum up, the improved PSPNet jujube trunk segmentation algorithm and trunk diameter measurement algorithm can accurately segment and measure the diameter in the natural environment, which provides a theoretical basis and technical support for the clamping of jujube harvesting robots.
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Oppong, Judith N., Clement E. Akumu, Samuel Dennis, and Stephanie Anyanwu. "Examining Deep Learning Pixel-Based Classification Algorithms for Mapping Weed Canopy Cover in Wheat Production Using Drone Data." Geomatics 5, no. 1 (2025): 4. https://doi.org/10.3390/geomatics5010004.
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Deep learning models offer valuable insights by leveraging large datasets, enabling precise and strategic decision-making essential for modern agriculture. Despite their potential, limited research has focused on the performance of pixel-based deep learning algorithms for detecting and mapping weed canopy cover. This study aims to evaluate the effectiveness of three neural network architectures—U-Net, DeepLabV3 (DLV3), and pyramid scene parsing network (PSPNet)—in mapping weed canopy cover in winter wheat. Drone data collected at the jointing and booting growth stages of winter wheat were used for the analysis. A supervised deep learning pixel classification methodology was adopted, and the models were tested on broadleaved weed species, winter wheat, and other weed species. The results show that PSPNet outperformed both U-Net and DLV3 in classification performance, with PSPNet achieving the highest overall mapping accuracy of 80%, followed by U-Net at 75% and DLV3 at 56.5%. These findings highlight the potential of pixel-based deep learning algorithms to enhance weed canopy mapping, enabling farmers to make more informed, site-specific weed management decisions, ultimately improving production and promoting sustainable agricultural practices.
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Khadijah, Nur Endah Sukmawati, Kusumaningrum Retno, Rismiyati, Sidik Sasongko Priyo, and Zainan Nisa Iffa. "Solid waste classification using pyramid scene parsing network segmentation and combined features." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 19, no. 6 (2021): 1902–12. https://doi.org/10.12928/telkomnika.v19i6.18402.
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Solid waste problem become a serious issue for the countries around the world since the amount of generated solid waste increase annually. As an effort to reduce and reuse of solid waste, a classification of solid waste image is needed to support automatic waste sorting. In the image classification task, image segmentation and feature extraction play important roles. This research applies recent deep leaning-based segmentation, namely pyramid scene parsing network (PSPNet). We also use various combination of image feature extraction (color, texture, and shape) to search for the best combination of features. As a comparison, we also perform experiment without using segmentation to see the effect of PSPNet. Then, support vector machine (SVM) is applied in the end as classification algorithm. Based on the result of experiment, it can be concluded that generally applying segmentation provide better source for feature extraction, especially in color and shape feature, hence increase the accuracy of classifier. It is also observed that the most important feature in this problem is color feature. However, the accuracy of classifier increase if additional features are introduced. The highest accuracy of 76.49% is achieved when PSPNet segmentation is applied and all combination of features are used.
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Nugraha, Deny Wiria, Amil Ahmad Ilham, Andani Achmad, and Ardiaty Arief. "Performance Improvement of Deep Convolutional Networks for Aerial Imagery Segmentation of Natural Disaster-Affected Areas." JOIV : International Journal on Informatics Visualization 7, no. 4 (2023): 2321. http://dx.doi.org/10.62527/joiv.7.4.1383.
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This study proposes a framework for improving performance and exploring the application of Deep Convolutional Networks (DCN) using the best parameters and criteria to accurately produce aerial imagery semantic segmentation of natural disaster-affected areas. This study utilizes two models: U-Net and Pyramid Scene Parsing Network (PSPNet). Extensive study results show that the Grid Search algorithm can improve the performance of the two models used, whereas previous research has not used the Grid Search algorithm to improve performance in aerial imagery segmentation of natural disaster-affected areas. The Grid Search algorithm performs parameter tuning on DCN, data augmentation criteria tuning, and dataset criteria tuning for pre-training. The most optimal DCN model is shown by PSPNet (152) (bpc), using the best parameters and criteria, with a mean Intersection over Union (mIoU) of 83.34%, a significant mIoU increase of 43.09% compared to using only the default parameters and criteria (baselines). The validation results using the k-fold cross-validation method on the most optimal DCN model produced an average accuracy of 99.04%. PSPNet(152) (bpc) can detect and identify various objects with irregular shapes and sizes, can detect and identify various important objects affected by natural disasters such as flooded buildings and roads, and can detect and identify objects with small shapes such as vehicles and pools, which are the most challenging task for semantic segmentation network models. This study also shows that increasing the network layers in the PSPNet-(18, 34, 50, 101, 152) model, which uses the best parameters and criteria, improves the model's performance. The results of this study indicate the need to utilize a special dataset from aerial imagery originating from the Unmanned Aerial Vehicle (UAV) during the pre-training stage for transfer learning to improve DCN performance for further research.
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Nugraha, Deny Wiria, Amil Ahmad Ilham, Andani Achmad, and Ardiaty Arief. "Performance Improvement of Deep Convolutional Networks for Aerial Imagery Segmentation of Natural Disaster-Affected Areas." JOIV : International Journal on Informatics Visualization 7, no. 4 (2023): 2321. http://dx.doi.org/10.30630/joiv.7.4.01383.
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This study proposes a framework for improving performance and exploring the application of Deep Convolutional Networks (DCN) using the best parameters and criteria to accurately produce aerial imagery semantic segmentation of natural disaster-affected areas. This study utilizes two models: U-Net and Pyramid Scene Parsing Network (PSPNet). Extensive study results show that the Grid Search algorithm can improve the performance of the two models used, whereas previous research has not used the Grid Search algorithm to improve performance in aerial imagery segmentation of natural disaster-affected areas. The Grid Search algorithm performs parameter tuning on DCN, data augmentation criteria tuning, and dataset criteria tuning for pre-training. The most optimal DCN model is shown by PSPNet (152) (bpc), using the best parameters and criteria, with a mean Intersection over Union (mIoU) of 83.34%, a significant mIoU increase of 43.09% compared to using only the default parameters and criteria (baselines). The validation results using the k-fold cross-validation method on the most optimal DCN model produced an average accuracy of 99.04%. PSPNet(152) (bpc) can detect and identify various objects with irregular shapes and sizes, can detect and identify various important objects affected by natural disasters such as flooded buildings and roads, and can detect and identify objects with small shapes such as vehicles and pools, which are the most challenging task for semantic segmentation network models. This study also shows that increasing the network layers in the PSPNet-(18, 34, 50, 101, 152) model, which uses the best parameters and criteria, improves the model's performance. The results of this study indicate the need to utilize a special dataset from aerial imagery originating from the Unmanned Aerial Vehicle (UAV) during the pre-training stage for transfer learning to improve DCN performance for further research.
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Han, Yanling, Bowen Zheng, Xianghong Kong, et al. "Underwater Fish Segmentation Algorithm Based on Improved PSPNet Network." Sensors 23, no. 19 (2023): 8072. http://dx.doi.org/10.3390/s23198072.
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With the sustainable development of intelligent fisheries, accurate underwater fish segmentation is a key step toward intelligently obtaining fish morphology data. However, the blurred, distorted and low-contrast features of fish images in underwater scenes affect the improvement in fish segmentation accuracy. To solve these problems, this paper proposes a method of underwater fish segmentation based on an improved PSPNet network (IST-PSPNet). First, in the feature extraction stage, to fully perceive features and context information of different scales, we propose an iterative attention feature fusion mechanism, which realizes the depth mining of fish features of different scales and the full perception of context information. Then, a SoftPool pooling method based on fast index weighted activation is used to reduce the numbers of parameters and computations while retaining more feature information, which improves segmentation accuracy and efficiency. Finally, a triad attention mechanism module, triplet attention (TA), is added to the different scale features in the golden tower pool module so that the space attention can focus more on the specific position of the fish body features in the channel through cross-dimensional interaction to suppress the fuzzy distortion caused by background interference in underwater scenes. Additionally, the parameter-sharing strategy is used in this process to make different scale features share the same learning weight parameters and further reduce the numbers of parameters and calculations. The experimental results show that the method presented in this paper yielded better results for the DeepFish underwater fish image dataset than other methods, with 91.56% for the Miou, 46.68 M for Params and 40.27 G for GFLOPS. In the underwater fish segmentation task, the method improved the segmentation accuracy of fish with similar colors and water quality backgrounds, improved fuzziness and small size and made the edge location of fish clearer.
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马, 帅. "CF-PSPnet Based Correction Study for Primary Beam Effect." Operations Research and Fuzziology 13, no. 06 (2023): 7757–67. http://dx.doi.org/10.12677/orf.2023.136758.
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Zhang, Jing. "Water Body Information Extraction from Remote Sensing Images based on PSPNet." International Journal of Computer Science and Information Technology 2, no. 1 (2024): 319–25. http://dx.doi.org/10.62051/ijcsit.v2n1.33.
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Remote sensing image has the characteristics of real-time, periodicity and wide monitoring range. It can quickly and accurately obtain water area, distribution and other information, which is of great significance to the utilization and development of water resources, agricultural irrigation, flood disaster assessment and so on. Since traditional water information extraction methods only use part of image band information, the accuracy of water information extraction is low and has certain limitations. In recent years, convolutional neural network technology has developed rapidly and achieved good results in water information extraction from remote sensing images. Therefore, in this paper, Pyramid Scene Parsing Neural Network (PSPNet) was used to extract water information from Ziyuan-3 multispectral remote sensing images, to make sample sets of water, and train the convolutional neural network model. Compared with the traditional normalized difference water index (NDWI) and support vector machine (SVM), the results show that PSPNet has the highest accuracy and the lowest misclassification rate.
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Xu, Yilin, Jie He, Yang Liu, Zilu Li, Weicong Cai, and Xiangang Peng. "Evaluation Method for Hosting Capacity of Rooftop Photovoltaic Considering Photovoltaic Potential in Distribution System." Energies 16, no. 22 (2023): 7677. http://dx.doi.org/10.3390/en16227677.
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Regarding the existing evaluation methods for photovoltaic (PV) hosting capacity in the distribution system that do not consider the spatial distribution of rooftop photovoltaic potential and are difficult to apply on the actual large-scale distribution systems, this paper proposes a PV hosting capacity evaluation method based on the improved PSPNet, grid multi-source data, and the CRITIC method. Firstly, an improved PSPNet is used to efficiently abstract the rooftop in satellite map images and then estimate the rooftop PV potential of each distribution substation supply area. Considering the safety, economy, and flexibility of distribution system operation, we establish a multi-level PV hosting capacity evaluation system. Finally, based on the rooftop PV potential estimation of each distribution substation supply area, we combine the multi-source data of the grid digitalization system to carry out security verification and indicator calculation and convert the indicator calculation results of each scenario into a comprehensive score through the CRITIC method. We estimate the rooftop photovoltaic potential and evaluate the PV hosting capacity of an actual 10 kV distribution system in Shantou, China. The results show that the improved PSPNet solves the hole problem of the original model and obtains a close-to-realistic rooftop photovoltaic potential estimation value. In addition, the proposed method considering the photovoltaic potential in this paper can more accurately evaluate the rooftop PV hosting capacity of the distribution system compared with the traditional method, which provides data support for the power grid corporation to formulate a reasonable PV development and hosting capacity enhancement program.
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Liu, Riming, and Zhenshan Gao. "Artificial Intelligence Precision Recognition and Auxiliary Diagnosis of Dental X-ray Panoramic Images Based on Deep Learning." BIO Web of Conferences 174 (2025): 03020. https://doi.org/10.1051/bioconf/202517403020.
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Objective: This study aims to explore the application of deep learning algorithms in dental X-ray panoramic images, particularly for the automatic segmentation of dental caries and identification of wisdom tooth types, in order to improve the accuracy and efficiency of dental diagnosis and assist doctors in formulating precise treatment plans. Methods: Multiple classic medical image segmentation network models (including Unet, PSPNet, FPN, Unet++, and DeepLabV3+) were trained and tested on the ParaDentCaries dataset to evaluate their performance in dental X-ray panoramic images. Performance was comprehensively compared using evaluation metrics such as IoU (Intersection over Union), Dice coefficient, sensitivity, accuracy, Hd95 (Hausdorff distance), and model parameters. Additionally, visualization methods were used to display the model’s prediction results across different lesion scales (small caries, medium caries, large caries). Results: The experimental results show that the Unet++ model performed best across all evaluation metrics, especially achieving strong results in IoU (58.94), Dice coefficient (72.71), sensitivity (68.03), and accuracy (82.00). Compared to other models (such as FPN, PSPNet, DeepLabV3+), Unet++ demonstrated clear advantages in detail recognition and boundary handling, particularly exhibiting higher accuracy in detecting small and medium caries. Visual analysis showed that Unet++ was able to accurately identify secondary carious areas, while PSPNet and DeepLabV3+ performed poorly in this regard, showing boundary detection deviations. Conclusion: The deep learning-based automatic diagnostic system for dental X-ray panoramic images, especially the Unet++ model, provides high-precision predictive results in dental caries segmentation and wisdom tooth type identification, significantly improving diagnostic efficiency and accuracy.
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Engineering, Mathematical Problems in. "Retracted: A Method of Image Semantic Segmentation Based on PSPNet." Mathematical Problems in Engineering 2023 (October 11, 2023): 1. http://dx.doi.org/10.1155/2023/9763027.
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Su, Xin, Ziguang Jia, Guangda Ma, Chunxu Qu, Tongtong Dai, and Liang Ren. "Image-Based Crack Detection Method for FPSO Module Support." Buildings 12, no. 8 (2022): 1147. http://dx.doi.org/10.3390/buildings12081147.
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Floating Production Storage and Offloading (FPSO) is essential offshore equipment for developing offshore oil and gas. Due to the complex sea conditions, FPSOs will be subjected to long-term alternate loads under some circumstances. Thus, it is inevitable that small cracks occur in the upper part of the module pier. Those cracks may influence the structure’s safety evaluation. Therefore, this paper proposes a method for the FPSO module to support crack identification based on the PSPNet model. The main idea is to introduce an attention mechanism into the model with Mobilenetv2 as the backbone of the PSPNet, which can fuse multiple feature maps and increase context information. The detail feature loss caused by multiple convolutions and compressions in the original model was solved by applying the proposed method. Moreover, the attention mechanism is introduced to enhance the extraction of adequate information and suppress invalid information. The mPA value and MIoU value of the improved model increased by 2.4% and 1.8%, respectively, through verification on FPSO datasets.
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Zhao, Di, Weiwei Zhang, and Yuxing Wang. "Research on Personnel Image Segmentation Based on MobileNetV2 H-Swish CBAM PSPNet in Search and Rescue Scenarios." Applied Sciences 14, no. 22 (2024): 10675. http://dx.doi.org/10.3390/app142210675.
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In post-disaster search and rescue scenarios, the accurate image segmentation of individuals is essential for efficient resource allocation and effective rescue operations. However, challenges such as image blur and limited resources complicate personnel segmentation. This paper introduces an enhanced, lightweight version of the Pyramid Scene Parsing Network (MHC-PSPNet). By substituting ResNet50 with the more efficient MobileNetV2 as the model backbone, the computational complexity is significantly reduced. Furthermore, replacing the ReLU6 activation function in MobileNetV2 with H-Swish enhances segmentation accuracy without increasing the parameter count. To further amplify high-level semantic features, global pooled features are fed into an attention mechanism network. The experimental results demonstrate that MHC-PSPNet performs exceptionally well on our custom dataset, achieving 97.15% accuracy, 89.21% precision, an F1 score of 94.53%, and an Intersection over Union (IoU) of 83.82%. Compared to the ResNet50 version, parameters are reduced by approximately 18.6 times, while detection accuracy improves, underscoring the efficiency and practicality of the proposed algorithm.
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Wu, Yanqiang, Yongbo Sun, Shuoqin Zhang, Xia Liu, Kai Zhou, and Jialin Hou. "A Size-Grading Method of Antler Mushrooms Using YOLOv5 and PSPNet." Agronomy 12, no. 11 (2022): 2601. http://dx.doi.org/10.3390/agronomy12112601.
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Quality grading in antler mushroom industrial production is a labor-intensive operation. For a long time, manual grading has been used for grading, which produces various problems such as insufficient reliability, low production efficiency, and high mushroom body damage. Automatic grading is a problem to be solved urgently for antler mushroom industrial development with increasing labor costs. To solve the problem, this paper deeply integrates the single-stage object detection of YOLOv5 and the semantic segmentation of PSPNet, and proposes a Y-PNet model for real-time object detection and an image segmentation network. This article also proposes an evaluation model for antler mushroom’s size, which eliminates subjective judgment and achieves quality grading. Moreover, to meet the needs of efficient and accurate hierarchical detection in the factory, this study uses the lightweight network model to construct a lightweight YOLOv5 single-stage object detection model. The MobileNetV3 network model embedded with a CBAM module is used as the backbone extractor in PSPNet to reduce the model’s size and improve the model’s efficiency and accuracy for segmentation. Experiments show that the proposed system can perform real-time grading successfully, which can provide instructive and practical references in industry.
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Qi, Xiaokang, Jingshi Dong, Yubin Lan, and Hang Zhu. "Method for Identifying Litchi Picking Position Based on YOLOv5 and PSPNet." Remote Sensing 14, no. 9 (2022): 2004. http://dx.doi.org/10.3390/rs14092004.
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China has the largest output of litchi in the world. However, at present, litchi is mainly picked manually, fruit farmers have high labor intensity and low efficiency. This means the intelligent unmanned picking system has broad prospects. The precise location of the main stem picking point of litchi is very important for the path planning of an unmanned system. Some researchers have identified the fruit and branches of litchi; however, there is relatively little research on the location of the main stem picking point of litchi. So, this paper presents a new open-access workflow for detecting accurate picking locations on the main stems and presents data used in the case study. At the same time, this paper also compares several different network architectures for main stem detection and segmentation and selects YOLOv5 and PSPNet as the most promising models for main stem detection and segmentation tasks, respectively. The workflow combines deep learning and traditional image processing algorithms to calculate the accurate location information of litchi main stem picking points in the litchi image. This workflow takes YOLOv5 as the target detection model to detect the litchi main stem in the litchi image, then extracts the detected region of interest (ROI) of the litchi main stem, uses PSPNet semantic segmentation model to semantically segment the ROI image of the main stem, carries out image post-processing operation on the ROI image of the main stem after semantic segmentation, and obtains the pixel coordinates of picking points in the ROI image of the main stem. After coordinate conversion, the pixel coordinates of the main stem picking points of the original litchi image are obtained, and the picking points are drawn on the litchi image. At present, the workflow can obtain the accurate position information of the main stem picking point in the litchi image. The recall and precision of this method were 76.29% and 92.50%, respectively, which lays a foundation for the subsequent work of obtaining the three-dimensional coordinates of the main stem picking point according to the image depth information, even though we have not done this work in this paper.
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Wang, Xi, Yongcun Guo, Shuang Wang, Gang Cheng, Xinquan Wang, and Lei He. "Rapid detection of incomplete coal and gangue based on improved PSPNet." Measurement 201 (September 2022): 111646. http://dx.doi.org/10.1016/j.measurement.2022.111646.
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Zhou, Jingchun, Mingliang Hao, Dehuan Zhang, Peiyu Zou, and Weishi Zhang. "Fusion PSPnet Image Segmentation Based Method for Multi-Focus Image Fusion." IEEE Photonics Journal 11, no. 6 (2019): 1–12. http://dx.doi.org/10.1109/jphot.2019.2950949.
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Gao, Rui, Jingfei Cao, Xiangang Cao, Jingyi Du, Hang Xue, and Daming Liang. "Wind Turbine Gearbox Gear Surface Defect Detection Based on Multiscale Feature Reconstruction." Electronics 12, no. 14 (2023): 3039. http://dx.doi.org/10.3390/electronics12143039.
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The fast and accurate detection of wind turbine gearbox surface defects is crucial for wind turbine maintenance and power security. However, owing to the uneven distribution of gear surface defects and the interference of complex backgrounds, there are limitations to gear-surface defect detection; therefore, this paper proposes a multiscale feature reconstruction-based detection method for wind turbine gearbox surface defects. First, the Swin Transformer was used as a backbone network based on the PSPNet network to obtain global and local features through multiscale feature reconstruction. Second, a Feature Similarity Module was used to filter important feature sub-blocks, which increased the inter-class differences and reduced the intra-class differences to enhance the discriminative ability of the model for similar features. Finally, the fusion of contextual information using the pyramid pooling module enhanced the extraction of gear surface defect features at different scales. The experimental results indicated that the improved algorithm outperformed the original PSPNet algorithm by 1.21% and 3.88% for the mean intersection over union and mean pixel accuracy, respectively, and significantly outperformed semantic segmentation networks such as U-Net and DeepLabv3+.
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Li, Shuaishuai, Xiang Gao, and Zexiao Xie. "Underwater Structured Light Stripe Center Extraction with Normalized Grayscale Gravity Method." Sensors 23, no. 24 (2023): 9839. http://dx.doi.org/10.3390/s23249839.
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The non-uniform reflectance characteristics of object surfaces and underwater environment disturbances during underwater laser measurements can have a great impact on laser stripe center extraction. Therefore, we propose a normalized grayscale gravity method to address this problem. First, we build an underwater structured light dataset for different illuminations, turbidity levels, and reflective surfaces of the underwater object and compare several state-of-the-art semantic segmentation models, including Deeplabv3, Deeplabv3plus, MobilenetV3, Pspnet, and FCNnet. Based on our comparison, we recommend PSPnet for the specific task of underwater structured light stripe segmentation. Second, in order to accurately extract the centerline of the extracted light stripe, the gray level values are normalized to eliminate the influence of noise and light stripe edge information on the centroids, and the weights of the cross-sectional extremes are increased to increase the function convergence for better robustness. Finally, the subpixel-structured light center points of the image are obtained by bilinear interpolation to improve the image resolution and extraction accuracy. The experimental results show that the proposed method can effectively eliminate noise interference while exhibiting good robustness and self-adaptability.
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Sowe, Ebou A., Mammy F. Sanyang, Wahib Yahya, and Hindolo George Gegbe. "Semantic Segmentation in Self-Driving Cars Using Pyramid Parsing Network (PSPNet) on Cityscape Dataset." European Journal of Applied Science, Engineering and Technology 3, no. 1 (2025): 87–98. https://doi.org/10.59324/ejaset.2025.3(1).07.
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Semantic segmentation has been one of the must research topics in the field of computer vision in recent years. This study was conducted using U-Net architecture in the context of self-driving cars on a cityscape dataset. The dataset is an urban scene image that contains all scene scenarios in a typical city. It includes 5,000 high-quality finely annotated pixel-level images gathered from 50 cities over various seasons. The proposed PSPNet model uses a pre-trained RestNet101 for feature extraction. We used a pyramid pooling of (1x1), (2x2), (3x3) and (6x6). We further used augmentation techniques to make the model learn more features of both the major and minor classes. The model achieved 90% accuracy, 83% pixel accuracy, 90% precision, 88% recall and 89% F1 score metric. The model was trained for 75 epochs of 3 hours of training time on the cityscape dataset. The model has shown good performance by achieving high accuracy and addressing class imbalance in the context of autonomous driving. Therefore, we concluded that PSPNet with RestNet101 as the backbone achieved high accuracy compared to the state-of-the-art model and addressed the issue of class imbalance.
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Sowe, Ebou A., Mammy F. Sanyang, Wahib Yahya, and Hindolo George Gegbe. "Semantic Segmentation in Self-Driving Cars Using Pyramid Parsing Network (PSPNet) on Cityscape Dataset." European Journal of Applied Science, Engineering and Technology 3, no. 1 (2025): 87–98. https://doi.org/10.59324/ejaset.2025.3(1).07.
Full textAbstract:
Semantic segmentation has been one of the must research topics in the field of computer vision in recent years. This study was conducted using U-Net architecture in the context of self-driving cars on a cityscape dataset. The dataset is an urban scene image that contains all scene scenarios in a typical city. It includes 5,000 high-quality finely annotated pixel-level images gathered from 50 cities over various seasons. The proposed PSPNet model uses a pre-trained RestNet101 for feature extraction. We used a pyramid pooling of (1x1), (2x2), (3x3) and (6x6). We further used augmentation techniques to make the model learn more features of both the major and minor classes. The model achieved 90% accuracy, 83% pixel accuracy, 90% precision, 88% recall and 89% F1 score metric. The model was trained for 75 epochs of 3 hours of training time on the cityscape dataset. The model has shown good performance by achieving high accuracy and addressing class imbalance in the context of autonomous driving. Therefore, we concluded that PSPNet with RestNet101 as the backbone achieved high accuracy compared to the state-of-the-art model and addressed the issue of class imbalance.
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Li Liangfu, 李良福, 王楠 Wang Nan, 武彪 Wu Biao та 张晰 Zhang Xi. "基于改进PSPNet的桥梁裂缝图像分割算法". Laser & Optoelectronics Progress 58, № 22 (2021): 2210001. http://dx.doi.org/10.3788/lop202158.2210001.
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Pan, Qian, Maofang Gao, Pingbo Wu, Jingwen Yan, and Shilei Li. "A Deep-Learning-Based Approach for Wheat Yellow Rust Disease Recognition from Unmanned Aerial Vehicle Images." Sensors 21, no. 19 (2021): 6540. http://dx.doi.org/10.3390/s21196540.
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Yellow rust is a disease with a wide range that causes great damage to wheat. The traditional method of manually identifying wheat yellow rust is very inefficient. To improve this situation, this study proposed a deep-learning-based method for identifying wheat yellow rust from unmanned aerial vehicle (UAV) images. The method was based on the pyramid scene parsing network (PSPNet) semantic segmentation model to classify healthy wheat, yellow rust wheat, and bare soil in small-scale UAV images, and to investigate the spatial generalization of the model. In addition, it was proposed to use the high-accuracy classification results of traditional algorithms as weak samples for wheat yellow rust identification. The recognition accuracy of the PSPNet model in this study reached 98%. On this basis, this study used the trained semantic segmentation model to recognize another wheat field. The results showed that the method had certain generalization ability, and its accuracy reached 98%. In addition, the high-accuracy classification result of a support vector machine was used as a weak label by weak supervision, which better solved the labeling problem of large-size images, and the final recognition accuracy reached 94%. Therefore, the present study method facilitated timely control measures to reduce economic losses.
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Yang, Qiong, and Lifeng Yu. "Recognition of Taxi Violations Based on Semantic Segmentation of PSPNet and Improved YOLOv3." Scientific Programming 2021 (November 29, 2021): 1–13. http://dx.doi.org/10.1155/2021/4520190.
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Taxi has the characteristics of strong mobility and wide dispersion, which makes it difficult for relevant law enforcement officers to make accurate judgment on their illegal acts quickly and accurately. With the investment of intelligent transportation system, image analysis technology has become a new method to determine the illegal behavior of taxis, but the current image analysis method is still difficult to support the detection of illegal behavior of taxis in the actual complex image scene. To solve this problem, this study proposed a method of taxi violation recognition based on semantic segmentation of PSPNet and improved YOLOv3. (1) Based on YOLOv3, the proposed method introduces spatial pyramid pooling (SPP) for taxi recognition, which can convert vehicle feature images with different resolutions into feature vectors with the same dimension as the full connection layer and solve the problem of repeated extraction of YOLOv3 vehicle image features. (2) This method can recognize two different violations of taxi (blocking license plate and illegal parking) rather than only one. (3) Based on PSPNet semantic segmentation network, a taxi illegal parking detection method is proposed. This method can collect the global information of road condition images and aggregate the image information of different regions, so as to improve the ability to obtain the global information orderly and improve the accuracy of taxi illegal parking detection. The experimental results show that the proposed method has excellent recognition performance for the detection rate of license plate occlusion behavior DR is 85.3%, and the detection rate of taxi illegal parking phenomenon DR is 96.1%.
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Yang, Shuang, Yuzhu Wang, Panzhe Wang, et al. "Automatic Identification of Landslides Based on Deep Learning." Applied Sciences 12, no. 16 (2022): 8153. http://dx.doi.org/10.3390/app12168153.
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A landslide is a kind of geological disaster with high frequency, great destructiveness, and wide distribution today. The occurrence of landslide disasters bring huge losses of life and property. In disaster relief operations, timely and reliable intervention measures are very important to prevent the recurrence of landslides or secondary disasters. However, traditional landslide identification methods are mainly based on visual interpretation and on-site investigation, which are time-consuming and inefficient. They cannot meet the time requirements in disaster relief operations. Therefore, to solve this problem, developing an automatic identification method for landslides is very important. This paper proposes such a method. We combined deep learning with landslide extraction from remote sensing images, used a semantic segmentation model to complete the automatic identification process of landslides and used the evaluation indicators in the semantic segmentation task (mean IoU [mIoU], recall, and precision) to measure the performance of the model. We selected three classic semantic segmentation models (U-Net, DeepLabv3+, PSPNet), tried to use different backbone networks for them and finally arrived at the most suitable model for landslide recognition. According to the experimental results, the best recognition accuracy of PSPNet is with the classification network ResNet50 as the backbone network. The mIoU is 91.18%, which represents high accuracy; Through this experiment, we demonstrated the feasibility and effectiveness of deep learning methods in landslide identification.
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Wang, Xiao, Di Wang, Chenghao Liu, et al. "Refined Intelligent Landslide Identification Based on Multi-Source Information Fusion." Remote Sensing 16, no. 17 (2024): 3119. http://dx.doi.org/10.3390/rs16173119.
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Landslides are most severe in the mountainous regions of southwestern China. While landslide identification provides a foundation for disaster prevention operations, methods for utilizing multi-source data and deep learning techniques to improve the efficiency and accuracy of landslide identification in complex environments are still a focus of research and a difficult issue in landslide research. In this study, we address the above problems and construct a landslide identification model based on the shifted window (Swin) transformer. We chose Ya’an, which has a complex terrain and experiences frequent landslides, as the study area. Our model, which fuses features from different remote sensing data sources and introduces a loss function that better learns the boundary information of the target, is compared with the pyramid scene parsing network (PSPNet), the unified perception parsing network (UPerNet), and DeepLab_V3+ models in order to explore the learning potential of the model and test the models’ resilience in an open-source landslide database. The results show that in the Ya’an landslide database, compared with the above benchmark networks (UPerNet, PSPNet, and DeepLab_v3+), the Swin Transformer-based optimization model improves overall accuracies by 1.7%, 2.1%, and 1.5%, respectively; the F1_score is improved by 14.5%, 16.2%, and 12.4%; and the intersection over union (IoU) is improved by 16.9%, 18.5%, and 14.6%, respectively. The performance of the optimized model is excellent.
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Sunwoo, Hasik, Seungwoo Lee, and Woojin Paik. "A Software-Defined Sensor System Using Semantic Segmentation for Monitoring Remaining Intravenous Fluids." Sensors 25, no. 10 (2025): 3082. https://doi.org/10.3390/s25103082.
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Accurate intravenous (IV) fluid monitoring is critical in healthcare to prevent infusion errors and ensure patient safety. Traditional monitoring methods often depend on dedicated hardware, such as weight sensors or optical systems, which can be costly, complex, and challenging to scale across diverse clinical settings. This study introduces a software-defined sensing approach that leverages semantic segmentation using the pyramid scene parsing network (PSPNet) to estimate the remaining IV fluid volumes directly from images captured by standard smartphones. The system identifies the IV container (vessel) and its fluid content (liquid) using pixel-level segmentation and estimates the remaining fluid volume without requiring physical sensors. Trained on a custom IV-specific image dataset, the proposed model achieved high accuracy with mean intersection over union (mIoU) scores of 0.94 for the vessel and 0.92 for the fluid regions. Comparative analysis with the segment anything model (SAM) demonstrated that the PSPNet-based system significantly outperformed the SAM, particularly in segmenting transparent fluids without requiring manual threshold tuning. This approach provides a scalable, cost-effective alternative to hardware-dependent monitoring systems and opens the door to AI-powered fluid sensing in smart healthcare environments. Preliminary benchmarking demonstrated that the system achieves near-real-time inference on mobile devices such as the iPhone 12, confirming its suitability for bedside and point-of-care use.
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Long, Xudong, Weiwei Zhang, and Bo Zhao. "PSPNet-SLAM: A Semantic SLAM Detect Dynamic Object by Pyramid Scene Parsing Network." IEEE Access 8 (2020): 214685–95. http://dx.doi.org/10.1109/access.2020.3041038.
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S.R.F, Natzina Juanita, Nadine Suzanne S.R.F, Shojaa Ayed Aljasar, Yubin Xu, and Muhammad Saqib. "ANAYLSIS AND DETECTION OF COMMUNITY-ACQUIRED PNEUMONIA USING PSPNET WITH COMPLEX DAUBECHIES WAVELETS." Indian Journal of Computer Science and Engineering 11, no. 3 (2020): 217–25. http://dx.doi.org/10.21817/indjcse/2020/v11i3/201103076.
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Huang, Liang, Xuequn Wu, Qiuzhi Peng, and Xueqin Yu. "Depth Semantic Segmentation of Tobacco Planting Areas from Unmanned Aerial Vehicle Remote Sensing Images in Plateau Mountains." Journal of Spectroscopy 2021 (March 1, 2021): 1–14. http://dx.doi.org/10.1155/2021/6687799.
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The tobacco in plateau mountains has the characteristics of fragmented planting, uneven growth, and mixed/interplanting of crops. It is difficult to extract effective features using an object-oriented image analysis method to accurately extract tobacco planting areas. To this end, the advantage of deep learning features self-learning is relied on in this paper. An accurate extraction method of tobacco planting areas based on a deep semantic segmentation model from the unmanned aerial vehicle (UAV) remote sensing images in plateau mountains is proposed in this paper. Firstly, the tobacco semantic segmentation dataset is established using Labelme. Four deep semantic segmentation models of DeeplabV3+, PSPNet, SegNet, and U-Net are used to train the sample data in the dataset. Among them, in order to reduce the model training time, the MobileNet series of lightweight networks are used to replace the original backbone networks of the four network models. Finally, the predictive images are semantically segmented by trained networks, and the mean Intersection over Union (mIoU) is used to evaluate the accuracy. The experimental results show that, using DeeplabV3+, PSPNet, SegNet, and U-Net to perform semantic segmentation on 71 scene prediction images, the mIoU obtained is 0.9436, 0.9118, 0.9392, and 0.9473, respectively, and the accuracy of semantic segmentation is high. The feasibility of the deep semantic segmentation method for extracting tobacco planting surface from UAV remote sensing images has been verified, and the research method can provide a reference for subsequent automatic extraction of tobacco planting areas.
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Li, Zechen, Shuqi Zhao, Yuxian Lu, Cheng Song, Rongyong Huang, and Kefu Yu. "Deep Learning-Based Automatic Estimation of Live Coral Cover from Underwater Video for Coral Reef Health Monitoring." Journal of Marine Science and Engineering 12, no. 11 (2024): 1980. http://dx.doi.org/10.3390/jmse12111980.
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Coral reefs are vital to marine biodiversity but are increasingly threatened by global climate change and human activities, leading to significant declines in live coral cover (LCC). Monitoring LCC is crucial for assessing the health of coral reef ecosystems and understanding their degradation and recovery. Traditional methods for estimating LCC, such as the manual interpretation of underwater survey videos, are labor-intensive and time-consuming, limiting their scalability for large-scale ecological monitoring. To overcome these challenges, this study introduces an innovative deep learning-based approach that utilizes semantic segmentation to automatically interpret LCC from underwater videos. That is, we enhanced PSPNet for live coral segmentation by incorporating channel and spatial attention mechanisms, along with pixel shuffle modules. Experimental results demonstrated that the proposed model achieved a mean Intersection over Union (mIoU) of 89.51% and a mean Pixel Accuracy (mPA) of 94.47%, showcasing superior accuracy in estimating LCC compared to traditional methods. Moreover, comparisons indicated that the proposed model aligns more closely with manual interpretations than other models, with an mean absolute error of 4.17%, compared to 5.89% for the original PSPNet, 6.03% for Deeplab v3+, 7.12% for U-Net, and 6.45% for HRNet, suggesting higher precision in LCC estimation. By automating the estimation of LCC, this deep learning-based approach can greatly enhance efficiency, thereby contributing significantly to global conservation efforts by enabling more scalable and efficient monitoring and management of coral reef ecosystems.
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Tang, Jiaming, Chunhua Chen, Zhiyong Huang, et al. "Crack Unet: Crack Recognition Algorithm Based on Three-Dimensional Ground Penetrating Radar Images." Sensors 22, no. 23 (2022): 9366. http://dx.doi.org/10.3390/s22239366.
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Three-dimensional (3D) ground-penetrating radar is an effective method for detecting internal crack damage in pavement structures. Inefficient manual interpretation of radar images and high personnel requirements have substantially restrained the generalization of 3D ground-penetrating radar. An improved Crack Unet model based on the Unet semantic segmentation model is proposed herein for 3D ground-penetrating radar crack image processing. The experiment showed that the MPA, MioU, and accuracy of the model were improved, and it displayed better capacity in the radar image crack segmentation task than current mainstream algorithms do, such as deepLabv3, PSPNet, and Unet. In the test dataset without cracks, Crack Unet is on the same level as deepLabv3 and PSPNet, which can meet engineering requirements and display a significant improvement compared with Unet. According to the ablation experiment, the MPA and MioU of Unet configured with PMDA, MC-FS, and RS modules were larger than those of Unet configured with one or two modules. The PMDA module adopted by the Crack Unet model showed a higher MPA and MioU than the SE module and the CBAM module did, respectively. The results show that the Crack Unet model has a better segmentation ability than the current mainstream algorithms do in the task of the crack segmentation of radar images, and the performance of crack segmentation is significantly improved compared with the Unet model. The Crack Unet model has excellent engineering application value in the task of the crack segmentation of radar images.
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Ma, Kaifeng, Xiang Meng, Mengshu Hao, Guiping Huang, Qingfeng Hu, and Peipei He. "Research on the Efficiency of Bridge Crack Detection by Coupling Deep Learning Frameworks with Convolutional Neural Networks." Sensors 23, no. 16 (2023): 7272. http://dx.doi.org/10.3390/s23167272.
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Bridge crack detection based on deep learning is a research area of great interest and difficulty in the field of bridge health detection. This study aimed to investigate the effectiveness of coupling a deep learning framework (DLF) with a convolutional neural network (CNN) for bridge crack detection. A dataset consisting of 2068 bridge crack images was randomly split into training, verification, and testing sets with a ratio of 8:1:1, respectively. Several CNN models, including Faster R-CNN, Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO)-v5(x), U-Net, and Pyramid Scene Parsing Network (PSPNet), were used to conduct experiments using the PyTorch, TensorFlow2, and Keras frameworks. The experimental results show that the Harmonic Mean (F1) values of the detection results of the Faster R-CNN and SSD models under the Keras framework are relatively large (0.76 and 0.67, respectively, in the object detection model). The YOLO-v5(x) model of the TensorFlow2 framework achieved the highest F1 value of 0.67. In semantic segmentation models, the U-Net model achieved the highest detection result accuracy (AC) value of 98.37% under the PyTorch framework. The PSPNet model achieved the highest AC value of 97.86% under the TensorFlow2 framework. These experimental results provide optimal coupling efficiency parameters of a DLF and CNN for bridge crack detection. A more accurate and efficient DLF and CNN model for bridge crack detection has been obtained, which has significant practical application value.
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Chen, Dong, Xianghong Li, Fan Hu, et al. "EDPNet: An Encoding–Decoding Network with Pyramidal Representation for Semantic Image Segmentation." Sensors 23, no. 6 (2023): 3205. http://dx.doi.org/10.3390/s23063205.
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This paper proposes an encoding–decoding network with a pyramidal representation module, which will be referred to as EDPNet, and is designed for efficient semantic image segmentation. On the one hand, during the encoding process of the proposed EDPNet, the enhancement of the Xception network, i.e., Xception+ is employed as a backbone to learn the discriminative feature maps. The obtained discriminative features are then fed into the pyramidal representation module, from which the context-augmented features are learned and optimized by leveraging a multi-level feature representation and aggregation process. On the other hand, during the image restoration decoding process, the encoded semantic-rich features are progressively recovered with the assistance of a simplified skip connection mechanism, which performs channel concatenation between high-level encoded features with rich semantic information and low-level features with spatial detail information. The proposed hybrid representation employing the proposed encoding–decoding and pyramidal structures has a global-aware perception and captures fine-grained contours of various geographical objects very well with high computational efficiency. The performance of the proposed EDPNet has been compared against PSPNet, DeepLabv3, and U-Net, employing four benchmark datasets, namely eTRIMS, Cityscapes, PASCAL VOC2012, and CamVid. EDPNet acquired the highest accuracy of 83.6% and 73.8% mIoUs on eTRIMS and PASCAL VOC2012 datasets, while its accuracy on the other two datasets was comparable to that of PSPNet, DeepLabv3, and U-Net models. EDPNet achieved the highest efficiency among the compared models on all datasets.
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Shabalina, D. E., K. S. Lanchukovskaya, T. V. Liakh, and K. V. Chaika. "Semantic Image Segmentation in Duckietown." Vestnik NSU. Series: Information Technologies 19, no. 3 (2021): 26–39. http://dx.doi.org/10.25205/1818-7900-2021-19-3-26-39.
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The article is devoted to evaluation of the applicability of existing semantic segmentation algorithms for the “Duckietown” simulator. The article explores classical semantic segmentation algorithms as well as ones based on neural networks. We also examined machine learning frameworks, taking into account all the limitations of the “Duckietown” simulator. According to the research results, we selected neural network algorithms based on U-Net, SegNet, DeepLab-v3, FC-DenceNet and PSPNet networks to solve the segmentation problem in the “Duckietown” project. U-Net and SegNet have been tested on the “Duckietown” simulator.
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Zhang, Yan, Weihong Li, Weiguo Gong, Zixu Wang, and Jingxi Sun. "An Improved Boundary-Aware Perceptual Loss for Building Extraction from VHR Images." Remote Sensing 12, no. 7 (2020): 1195. http://dx.doi.org/10.3390/rs12071195.
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With the development of deep learning technology, an enormous number of convolutional neural network (CNN) models have been proposed to address the challenging building extraction task from very high-resolution (VHR) remote sensing images. However, searching for better CNN architectures is time-consuming, and the robustness of a new CNN model cannot be guaranteed. In this paper, an improved boundary-aware perceptual (BP) loss is proposed to enhance the building extraction ability of CNN models. The proposed BP loss consists of a loss network and transfer loss functions. The usage of the boundary-aware perceptual loss has two stages. In the training stage, the loss network learns the structural information from circularly transferring between the building mask and the corresponding building boundary. In the refining stage, the learned structural information is embedded into the building extraction models via the transfer loss functions without additional parameters or postprocessing. We verify the effectiveness and efficiency of the proposed BP loss both on the challenging WHU aerial dataset and the INRIA dataset. Substantial performance improvements are observed within two representative CNN architectures: PSPNet and UNet, which are widely used on pixel-wise labelling tasks. With BP loss, UNet with ResNet101 achieves 90.78% and 76.62% on IoU (intersection over union) scores on the WHU aerial dataset and the INRIA dataset, respectively, which are 1.47% and 1.04% higher than those simply trained with the cross-entropy loss function. Additionally, similar improvements (0.64% on the WHU aerial dataset and 1.69% on the INRIA dataset) are also observed on PSPNet, which strongly supports the robustness of the proposed BP loss.
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McCall, Hugh, Janine Beahm, Caeleigh Landry, Ziyin Huang, R. Nicholas Carleton, and Heather Hadjistavropoulos. "How Have Public Safety Personnel Seeking Digital Mental Healthcare Been Affected by the COVID-19 Pandemic? An Exploratory Mixed Methods Study." International Journal of Environmental Research and Public Health 17, no. 24 (2020): 9319. http://dx.doi.org/10.3390/ijerph17249319.
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Public safety personnel (PSP) experience unique occupational stressors and suffer from high rates of mental health problems. The COVID-19 pandemic has impacted virtually all aspects of human life around the world and has introduced additional occupational stressors for PSP. The objective of this study was to explore how PSP, especially those seeking digital mental health services, have been affected by the pandemic. Our research unit, PSPNET, provides internet-delivered cognitive behavioral therapy to PSP in the Canadian province of Saskatchewan. When the pandemic spread to Saskatchewan, PSPNET began inquiring about the impact of the pandemic on prospective clients during the eligibility screening process. We used content analysis to analyze data from telephone screening interviews (n = 56) and descriptive statistics to analyze data from a questionnaire concerning the impacts of COVID-19 (n = 41). The results showed that most PSP reported facing several novel emotional challenges (e.g., social isolation, boredom, anger, and fear) and logistical challenges (e.g., related to childcare, finances, work, and access to mental healthcare). Most participants indicated they felt at least somewhat afraid of contracting COVID-19 but felt more afraid of their families contracting the virus than themselves. However, few participants reported severe challenges of any kind, and many (40%) indicated that they had not been significantly negatively impacted by the pandemic. Overall, the results suggest that PSP are not expressing significant concern at this time in meeting the novel challenges posed by COVID-19. Continued research will be required to monitor how diverse PSP populations and treatment outcomes are affected by the pandemic as the situation evolves.
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Trivedi, Manushi, Yuwei Zhou, Jonathan Hyun Moon, et al. "A Preliminary Method for Tracking In-Season Grapevine Cluster Closure Using Image Segmentation and Image Thresholding." Australian Journal of Grape and Wine Research 2023 (September 30, 2023): 1–12. http://dx.doi.org/10.1155/2023/3923839.
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Mapping and monitoring cluster morphology provides insights for disease risk assessment, quality control in wine production, and understanding environmental influences on cluster shape. During the progression of grapevine morphology, cluster closure (CC) (also called bunch closure) is the stage when berries touch one another. This study used mobile phone images to develop a direct quantification method for tracking CC in three grapevine cultivars (Riesling, Pinot gris, and Cabernet Franc). A total of 809 cluster images from fruit set to veraison were analyzed using two image segmentation methods: (i) a Pyramid Scene Parsing Network (PSPNet) to extract cluster boundaries and (ii) Otsu’s image thresholding method to calculate % CC based on gaps between the berries. PSPNet produced high accuracy (mean accuracy = 0.98, mean intersection over union (mIoU) = 0.95) with mIoU > 0.90 for both cluster and noncluster classes. Otsu’s thresholding method resulted in <2% falsely classified gap and berry pixels affecting quantified % CC. The progression of CC was described using basic statistics (mean and standard deviation) and using a curve fit. The CC curve showed an asymptotic trend, with a higher rate of progression observed in the first three weeks, followed by a gradual approach towards an asymptote. We propose that the X value (in this example, number of weeks past berry set) at which the CC progression curve reaches the asymptote be considered as the official phenological stage of CC. The developed method provides a continuous scale of CC throughout the season, potentially serving as a valuable open-source research tool for studying grapevine cluster phenology and factors affecting CC.
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Nzelibe, I.U., and T.E. Akinboyewa. "An Appraisal of Deep Learning Algorithms in Automatic Building Footprint Extraction from High-Resolution Satellite Image in Parts of Akure, Nigeria." Nigerian Research Journal of Engineering and Environmental Sciences 9, no. 1 (2024): 129–44. https://doi.org/10.5281/zenodo.12599520.
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<em>The accuracy of spatial objects extracted from raster images using automatic feature extraction algorithms remains a problem in the field of remote sensing. This study assesses the performance of three existing deep learning algorithms, viz: DeepLabV3, Pyramid Scene Parsing Network (PSPNET), and U-Network (U-NET)<strong> </strong>in extracting Building Footprints (BFP) from High-Resolution Satellite Images (HRSI). The assessment was performed on two study sites, High Building Densities (HBD) and Low Building Densities (LBD) areas, both located in the city of Akure, Ondo State, Nigeria. The HRSI used is the Google satellite image. Ground truthing data used as reference was derived by manually digitizing the HRSI after preprocessing. Results from the study reveal that UNet recorded the highest overall classification accuracy having values of 90.7 % and 97.6 % in HBD and LBD sites respectively. In terms of the area, the U Net recorded a Root Mean Square (RMS) error of 0.3 m<sup>2 </sup>and 0.2 m<sup>2</sup> in HBD and LBD sites respectively. In terms of positional accuracy, the UNet recorded a mean Euclidean BFP centroid displacement of 1.4 m and 0.9 m in HBD and LBD sites respectively. The UNet model indicated the best accuracy compared to DeepLabv3, and PSPNet in automatic BFP extraction, performing better in the LBD sites compared to the HBD sites. Based on the study, the UNET model is found to be best suited for building types both in HBD and LBD development in and around the city of Akure, Nigeria</em><em>.</em>
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Yu, Jun, Tao Cheng, Ning Cai, et al. "Wheat Lodging Segmentation Based on Lstm_PSPNet Deep Learning Network." Drones 7, no. 2 (2023): 143. http://dx.doi.org/10.3390/drones7020143.
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Lodging is one of the major issues that seriously affects wheat quality and yield. To obtain timely and accurate wheat lodging information and identify the potential factors leading to lodged wheat in wheat breeding programs, we proposed a lodging-detecting model coupled with unmanned aerial vehicle (UAV) image features of wheat at multiple plant growth stages. The UAV was used to collect canopy images and ground lodging area information at five wheat growth stages. The PSPNet model was improved by combining the convolutional LSTM (ConvLSTM) timing model, inserting the convolutional attention module (CBAM) and the Tversky loss function. The effect of the improved PSPNet network model in monitoring wheat lodging under different image sizes and different growth stages was investigated. The experimental results show that (1) the improved Lstm_PSPNet model was more effective in lodging prediction, and the precision reached 0.952; (2) choosing an appropriate image size could improve the segmentation accuracy, with the optimal image size in this study being 468 × 468; and (3) the model of Lstm_PSPNet improved its segmentation accuracy sequentially from early flowering to late maturity, and the three evaluation metrics increased sequentially from 0.932 to 0.952 for precision, from 0.912 to 0.940 for recall, and from 0.922 to 0.950 for F1-Score, with good extraction at mid and late reproductive stages. Therefore, the lodging information extraction model proposed in this study can make full use of temporal sequence features to improve image segmentation accuracy and effectively extract lodging areas at different growth stages. The model can provide more comprehensive reference and technical support for monitoring the lodging of wheat crops at different growth stages.
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Добровська, Людмила, та Ярослав Назарага. "ЗГОРТКОВА НЕЙРОННА МЕРЕЖА ДЛЯ СЕГМЕНТАЦІЇ СУДИН СІТКІВКИ ОКА". Біомедична інженерія і технологія, № 11 (28 вересня 2023): 31–44. http://dx.doi.org/10.20535/2617-8974.2023.11.288109.
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Важливе значення для постановки діагнозу при різних офтальмологічних захворюваннях відіграє дослідження, моніторинг та оцінка судин сітківки ока. Ідентифікація конкретних об’єктiв-патологій на зображеннях зводиться до розв’язання задач сегментації. Сегментація судин сітківки є ключовим кроком до точної візуалізації, діагностики захворювань ока, раннього лікування та планування хірургічного втручання. Саме тому важливою задачею є автоматизована сегментація судин сітківки. Мета даної роботи полягала у розробці програмного застосунку для сегментації зображень судин сітківки ока з використанням машинного навчання у вигляді згорткової нейронної мережі. Наразі найточнішими є нейромережеві методи сегментації, а саме методи на основі глибокого навчання. База знімків, яка використовувалась для дослідження, була взята з загальнодоступного набору даних DRIVE, що надає еталонні сегментації (маски) для кожного зображення, для половини з яких застосовано аугментацію. Це надає змогу обчислити оцінки продуктивності моделі. Під час першого етапу дослідження було встановлено, що наразі до найпоширеніших:
 1) критеріїв, за якими кількісно можна оцінити якість сегментації, належать такі метрики: intersection over union; аccuracy; precision; sensitivity; specificity; F1-score; dice coefficient; loss function;
 2) мереж, здатних виконувати сегментацію зображень, належать такі: FCN, SegNet, U-Net, FC-Densenet, E-Net, Link-Net, RefineNet, PSPNet.
 Для вирішення задачі сегментації зображень судин сітківки ока виконано порівняння вказаних мереж за точністю, перевагами та обмеженнями. Встановлено, що враховуючи точність, найкраще для вирішення вказаної задачі підходять мережі DeepLab, PSPNet, U-Net. Другий етап дослідження полягав у розробці програмного застосунку (ПЗ), порівнянні та оцінюванні показників якості відомих систем сегментації судин сітківки та розробленого ПЗ. Результатом дослідження є розробка ПЗ, який надає такі оцінки за метриками: accuracy=0.9452, sensitivity = 0.8991, specificity= 0.9468, dice= 0.8247. Ці показники якості отримано під час роботи розробленого ПЗ.
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Ahmadi, Seyed Ali, and Ali Mohammadzadeh. "Flood detection in UAV images using PSPNet and uncertainty quantification with Monte-Carlo Dropout technique." Journal of Geomatics Science and Technology 13, no. 4 (2024): 41–56. http://dx.doi.org/10.61186/jgst.13.4.41.
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Carpenter, Chris. "Machine-Learning Techniques Classify, Quantify Cuttings Lithology." Journal of Petroleum Technology 76, no. 01 (2024): 92–94. http://dx.doi.org/10.2118/0124-0092-jpt.
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_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 22867, “Automatic Lithology Classification of Cuttings With Deep Learning,” by Takashi Nanjo, Akira Ebitani, and Kazuaki Ishikawa, Japan Organization for Metals and Energy Security, et al. The paper has not been peer reviewed. Copyright 2023 International Petroleum Technology Conference. Reproduced by permission. _ Wellsite geologists spend approximately 70% of their time on cuttings descriptions. In addition, two or three wellsite geologists generally are assigned to a drilling campaign, to be replaced at the end of a shift. Machine-learning (ML) and artificial-intelligence (AI) techniques have the potential to solve these issues because of their advantages in prediction speed, objectivity, and consistency. The authors’ aim is to automate the task of cuttings descriptions with these techniques. A trained model for cuttings description has the potential to realize quantitative, high-speed cuttings description. Methods and Materials Six wells were selected for this study (Poseidon-1, Ichthys2A ST-1, Ichthys2A ST-2, Dinichthys north1, Ichthys north1, and Ichthys north1 ST1). The wells were drilled in the Browse Basin through various lithologies. The authors focused on four lithologies (carbonate, sandstone, mudstone, and volcanic) and collected the cuttings from these lithologies. The total number of cuttings was 160 (21 carbonate, 58 sandstone, 52 mudstone, and 29 volcanic). The cuttings were used in their original dry condition. Each cutting was transported to a Petri plate in a random and sparse layout using tweezers. A stereomicroscope and a digital camera were prepared. Magnification was 6.3X, and picture size was 1,600 pixels in width and 1,200 pixels in height. Pictures were taken randomly, with approximately 20 photographs for each sample. The annotation was conducted by geologists using annotation software. Five label classes were used (sandstone, mudstone, carbonate, volcanic, and background). The total number of annotation data was 1,978. The annotated data were split as training label data, validation label data, and test label data. The pictures were also split as training data, validation data, and test data. The picture and label data were paired. In this study, 1,496 pictures and label data were used as training data sets, 320 pictures and label data were used as validation data, and 162 pictures and label data were used as test data. Four architectures of semantic segmentation were created (PSPNet, Unet, FPN, and Linknet), including two different networks (ResNet152 and EfficientNetB7), and training models were created. The total number of trained models was eight. As a result, the combination of PSPNet and EfficientNetB7 showed the best performance. The combination of PSPNet and EfficientNetB7 was chosen as the architecture of this project. Hyperparameters were chosen in the best practice case. Mean intersection over union (IOU) was used as the model evaluation index.