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Journal articles on the topic 'PSPNet'

Author: Grafiati

Published: 25 June 2021

Last updated: 29 July 2025

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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

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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 pro

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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 gro

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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 s

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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 par

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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&

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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: c

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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 Convolutio

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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

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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 combinatio

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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

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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

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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 featur

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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

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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

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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 wa

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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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19

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

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20

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 enhan

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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 i

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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

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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. Sec

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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 r

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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 techniq

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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 techniq

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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 h

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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 semanti

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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

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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

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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 contain

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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 semant

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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-ba

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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 ta

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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

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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 aggreg

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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 t

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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 bounda

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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, PSPNE

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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 metho

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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

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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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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

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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 ма

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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

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