Journal articles on the topic 'Underwater object detection'
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Shen, Jie, Zhenxin Xu, Zhe Chen, Huibin Wang, and Xiaotao Shi. "Optical Prior-Based Underwater Object Detection with Active Imaging." Complexity 2021 (April 27, 2021): 1–12. http://dx.doi.org/10.1155/2021/6656166.
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Underwater object detection plays an important role in research and practice, as it provides condensed and informative content that represents underwater objects. However, detecting objects from underwater images is challenging because underwater environments significantly degenerate image quality and distort the contrast between the object and background. To address this problem, this paper proposes an optical prior-based underwater object detection approach that takes advantage of optical principles to identify optical collimation over underwater images, providing valuable guidance for extracting object features. Unlike data-driven knowledge, the prior in our method is independent of training samples. The fundamental novelty of our approach lies in the integration of an image prior and the object detection task. This novelty is fundamental to the satisfying performance of our approach in underwater environments, which is demonstrated through comparisons with state-of-the-art object detection methods.
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V, Karthikeyan. "Underwater Object Detection." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (May 31, 2020): 2091–95. http://dx.doi.org/10.22214/ijraset.2020.5344.
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Maccabee, Bruce S. "Underwater object detection system." Journal of the Acoustical Society of America 91, no. 5 (May 1992): 3081. http://dx.doi.org/10.1121/1.402901.
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Mahavarkar, Avinash, Ritika Kadwadkar, Sneha Maurya, and Smitha Raveendran. "Underwater Object Detection using Tensorflow." ITM Web of Conferences 32 (2020): 03037. http://dx.doi.org/10.1051/itmconf/20203203037.
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Object Detection is a popular technology that detects instances within an image. In order to eliminate the barriers in Computer Vision technology due to the dissolution of the BGR(Blue-Green-Red) constituents with the increase in depth, it has been a necessity that the accuracy and efficiency of detecting any object underwater is optimum. In this article, we conduct Underwater Object Detection using Machine Learning through Tensorflow and Image Processing along with Faster R-CNN (Regions with Convolution Neural Network) as an algorithm for implementation. A suitable environment will be created so that Machine Learning algorithm will be used to train different images of the object. Open source Computer Vision has various functions which can be used for the image processing needs when an image is captured.
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Zhang, Yangmei. "Application of Smart Sensor in Underwater Weak Object Detection and Positioning." Wireless Communications and Mobile Computing 2021 (December 23, 2021): 1–16. http://dx.doi.org/10.1155/2021/5791567.
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This paper is aimed at studying underwater object detection and positioning. Objects are detected and positioned through an underwater scene segmentation-based weak object detection algorithm and underwater positioning technology based on the three-dimensional (3D) omnidirectional magnetic induction smart sensor. The proposed weak object detection involves a predesigned U-shaped network- (U-Net-) architectured image segmentation network, which has been improved before application. The key factor of underwater positioning technology based on 3D omnidirectional magnetic induction is the magnetic induction intensity. The results show that the image-enhanced object detection method improves the accuracy of Yellow Croaker, Goldfish, and Mandarin Fish by 3.2%, 1.5%, and 1.6%, respectively. In terms of sensor positioning technology, under the positioning Signal-to-Noise Ratio (SNR) of 15 dB and 20 dB, the curve trends of actual distance and positioning distance are consistent, while SNR = 10 dB , the two curves deviate greatly. The research conclusions read as follows: an underwater scene segmentation-based weak object detection method is proposed for invalid underwater object samples from poor labeling, which can effectively segment the background from underwater objects, remove the negative impact of invalid samples, and improve the precision of weak object detection. The positioning model based on a 3D coil magnetic induction sensor can obtain more accurate positioning coordinates. The effectiveness of 3D omnidirectional magnetic induction coil underwater positioning technology is verified by simulation experiments.
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ZHANG, Yan, Xingshan LI, Yemei SUN, and Shudong LIU. "Underwater object detection algorithm based on channel attention and feature fusion." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 40, no. 2 (April 2022): 433–41. http://dx.doi.org/10.1051/jnwpu/20224020433.
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Due to the color deviation, low contrast and fuzzy object in underwater optical images, there are some problems in underwater object detection, such as missed detection and false detection. In order to solve the above-mentioned problems, an underwater object detection algorithm is proposed based on the channel attention and feature fusion for underwater optical images. The excitation residual module is designed based on the channel attention, and the forward propagation feature information is adaptively allocated weights to highlight the salience of different channel feature maps, which improves the network ability to extract high-frequency information from the underwater images. The multi-scale feature fusion module is designed to add a large scale feature map for object detection, which improves the detection performance of the network for small size objects by using its corresponding small size receptive field, and further improves the detection accuracy of the network for different size objects in the underwater environment. To improve the generalization performance of the network to the underwater environment, the data augmentation method based on the stitching and fusion is designed to simulate the overlap, occlusion and blurring of underwater objects, which improves the adaptability of the network to the underwater environment. Through experiments on the public dataset URPC, the algorithm in this paper improves the mean average precision by 5.42%, 3.20% and 0.9% compared with YOLOv3, YOLOv4 and YOLOv5, respectively, effectively improving the missed and false detection of objects of different sizes in complex underwater environments.
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Shen, Jie, Tanghuai Fan, Min Tang, Qian Zhang, Zhen Sun, and Fengchen Huang. "A Biological Hierarchical Model Based Underwater Moving Object Detection." Computational and Mathematical Methods in Medicine 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/609801.
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Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results.
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Karimanzira, Divas, Helge Renkewitz, David Shea, and Jan Albiez. "Object Detection in Sonar Images." Electronics 9, no. 7 (July 21, 2020): 1180. http://dx.doi.org/10.3390/electronics9071180.
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The scope of the project described in this paper is the development of a generalized underwater object detection solution based on Automated Machine Learning (AutoML) principles. Multiple scales, dual priorities, speed, limited data, and class imbalance make object detection a very challenging task. In underwater object detection, further complications come in to play due to acoustic image problems such as non-homogeneous resolution, non-uniform intensity, speckle noise, acoustic shadowing, acoustic reverberation, and multipath problems. Therefore, we focus on finding solutions to the problems along the underwater object detection pipeline. A pipeline for realizing a robust generic object detector will be described and demonstrated on a case study of detection of an underwater docking station in sonar images. The system shows an overall detection and classification performance average precision (AP) score of 0.98392 for a test set of 5000 underwater sonar frames.
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Wang, Jinkang, Xiaohui He, Faming Shao, Guanlin Lu, Qunyan Jiang, Ruizhe Hu, and Jinxin Li. "A Novel Attention-Based Lightweight Network for Multiscale Object Detection in Underwater Images." Journal of Sensors 2022 (September 7, 2022): 1–14. http://dx.doi.org/10.1155/2022/2582687.
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Underwater images have low quality, and underwater targets have different sizes. The mainstream target detection networks cannot achieve good results in detecting objects from underwater images. In this study, a lightweight underwater multiscale target detection model with an attention mechanism is designed to solve the above problems. In this model, MobileNetv3 is used as the backbone network for preliminary feature extraction. The lightweight feature extraction module (LFEM) pays attention to the feature map at the channel and space levels. The features with large weights are promoted, while the features with small weights are suppressed. Meanwhile, cross-group information exchange enriches the semantic information and location information of the objects. The context aggregation module (CIAM) pools the extracted feature maps to obtain feature pyramids, and it uses the upsampling-feature refinement-cascade addition (URC) method to effectively fuse global context information and enhance the feature representation. The scale normalization for feature pyramids (SNFP) performs adaptive multiscale perception and multianchor detection on feature maps to cover objects of different sizes and realize multiscale object detection in underwater images. The proposed network can realize lightweight feature extraction, effectively handle the global relationship between the underwater scene and the object while expanding the receptive field, traverse the objects of different scales, and achieve adaptive multianchor detection of multiscale objects in underwater images. The experimental results indicate that our method achieves an average accuracy of 81.94% and a detection speed of 44.3 FPS on a composite dataset. Also, our method is better than the mainstream object detection networks in terms of detection accuracy, lightweight design, and real-time performance.
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Wulandari, Nurcahyani, Igi Ardiyanto, and Hanung Adi Nugroho. "A Comparison of Deep Learning Approach for Underwater Object Detection." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 2 (April 20, 2022): 252–58. http://dx.doi.org/10.29207/resti.v6i2.3931.
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In recent year, marine ecosystems and fisheries becomes potential resources, therefore, monitoring of these objects will be important to ensure their existence. One of computer vision techniques, it is object detection, utilized to recognize and localize objects in underwater scenery. Many studies have been conducted to investigate various deep learning methods implemented in underwater object detection; however, only a few investigations have been performed to compare mainstream object detection algorithms in these circumstances. This article examines various state-of-the-art deep learning methods applied to underwater object detection, including Faster-RCNN, SSD, RetinaNet, YOLOv3, and YOLOv4. We trained five models on RUIE dataset, then the average detection time used to compare how fast a model can detect object within an image; and mAP also applied to measured detection accuracy. All trained models have costs and benefits; SSD was fast but had poor performance; RetinaNet had consistent performance across different thresholds but the detection speed was slow; YOLOv3 was the fastest and had sufficient performance comparable with RetinaNet; YOLOv4 was good at first but performance dropped as threshold enlargement; also, YOLOv4 needed extra time to detect objects compared to YOLOv3. There are no models that are fully suited for underwater object detection; nonetheless, when the mAP and average detection time of the five models were compared, we determined that YOLOv3 is the best acceptable model among the evaluated underwater object detection models.
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Gadzhiev, А. А., R. А. Eminov, and Kh G. Asadov. "Solution to the problem of minimum distance detection of various objects in shallow water depth." Transactions of the Krylov State Research Centre 2, no. 400 (May 16, 2022): 147–52. http://dx.doi.org/10.24937/2542-2324-2022-2-400-147-152.
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Object and purpose of research. The object of research is arrangement of various items on the bottom of water bodies. The purpose of research is achieving maximum invisibility for such items. Anticipated search or accidental detection of bottom objects can be carried out by the bathymetric method, i.e. assessment of water column over such objects. Materials and methods. It is expected that low flying UAVs (unmanned aerial vehicles) equipped with bathymetric laser emitter are used for detection of underwater objects. With consideration of some simplifications, optimization is carried out for the operation of bathymetric laser detector of objects located on the sea bed. The task of detecting an underwater object is considered to be solved when the difference between the signals reflected from the sea bed and underwater object is reliably recorded. Minimum external detectability is achieved at minimum of the said difference. Main results. An objective functional is obtained to characterize the total signal from multiple underwater objects. As a result of performed optimization, the condition is determined at which the minimum of the objective functional is achieved. According to the obtained result, the total signal reflected from an underwater object reaches its minimum if the height of underwater objects and their reflection coefficient vary paraphase, i.e. the growth of one is accompanied with the reduction of the other. Conclusion. A method for minimum detectability of objects stacked on the sea bed is suggested and validated. Practical procedures of storing various objects on the sea bed are defined, which ensures their invisibility for the bathymetric laser detector onboard the low-flying UAV.
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Zavyalov, A., and Yu Patrakov. "Universal assessment method for laser detection probability of sunken engineering structures." Transactions of the Krylov State Research Centre 1, no. 399 (March 15, 2022): 176–88. http://dx.doi.org/10.24937/2542-2324-2022-1-399-176-188.
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Object and purpose of research. Laser diagnostics, analysis of reflected laser signal from fixed underwater objects, improvement of laser optical methods, technologies and tools for underwater object studies, development of laser detection systems, determination of laser indication probability for fixed underwater objects. Materials and methods. Laser detection systems, analytical and computational methods, software programs, analytical tools for measurement data processing, laser diagnostics of reflected laser signal from underwater objects taking into account dissipation and absorption in atmosphere and hydrosphere. Main results. Improvement of technology and methods for diagnostics of reflected laser signal (back-scattering), determination of reliable detection limits for laser signals reflected from fixed underwater objects, software programs, laser diagnostics, efficiency analysis of laser detection systems. Conclusion. The study showed that high efficiency of laser detection systems considerably depends on the selected tech- nology, reflected laser signal display and processing techniques, and also on the probabilistic methods adopted to estimate the reflected signal luminance and the knowledge of hydrodsphere parameters.
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Han, Fenglei, Jingzheng Yao, Haitao Zhu, and Chunhui Wang. "Underwater Image Processing and Object Detection Based on Deep CNN Method." Journal of Sensors 2020 (May 22, 2020): 1–20. http://dx.doi.org/10.1155/2020/6707328.
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Due to the importance of underwater exploration in the development and utilization of deep-sea resources, underwater autonomous operation is more and more important to avoid the dangerous high-pressure deep-sea environment. For underwater autonomous operation, the intelligent computer vision is the most important technology. In an underwater environment, weak illumination and low-quality image enhancement, as a preprocessing procedure, is necessary for underwater vision. In this paper, a combination of max-RGB method and shades of gray method is applied to achieve the enhancement of underwater vision, and then a CNN (Convolutional Neutral Network) method for solving the weakly illuminated problem for underwater images is proposed to train the mapping relationship to obtain the illumination map. After the image processing, a deep CNN method is proposed to perform the underwater detection and classification, according to the characteristics of underwater vision, two improved schemes are applied to modify the deep CNN structure. In the first scheme, a 1∗1 convolution kernel is used on the 26∗26 feature map, and then a downsampling layer is added to resize the output to equal 13∗13. In the second scheme, a downsampling layer is added firstly, and then the convolution layer is inserted in the network, the result is combined with the last output to achieve the detection. Through comparison with the Fast RCNN, Faster RCNN, and the original YOLO V3, scheme 2 is verified to be better in detecting underwater objects. The detection speed is about 50 FPS (Frames per Second), and mAP (mean Average Precision) is about 90%. The program is applied in an underwater robot; the real-time detection results show that the detection and classification are accurate and fast enough to assist the robot to achieve underwater working operation.
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Wang, Zhixin, Peng Xu, Bohan Liu, Yankun Cao, Zhi Liu, and Zhaojun Liu. "Hyperspectral imaging for underwater object detection." Sensor Review 41, no. 2 (April 5, 2021): 176–91. http://dx.doi.org/10.1108/sr-07-2020-0165.
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Purpose This paper aims to demonstrate the principle and practical applications of hyperspectral object detection, carry out the problem we now face and the possible solution. Also some challenges in this field are discussed. Design/methodology/approach First, the paper summarized the current research status of the hyperspectral techniques. Then, the paper demonstrated the development of underwater hyperspectral techniques from three major aspects, which are UHI preprocess, unmixing and applications. Finally, the paper presents a conclusion of applications of hyperspectral imaging and future research directions. Findings Various methods and scenarios for underwater object detection with hyperspectral imaging are compared, which include preprocessing, unmixing and classification. A summary is made to demonstrate the application scope and results of different methods, which may play an important role in the application of underwater hyperspectral object detection in the future. Originality/value This paper introduced several methods of hyperspectral image process, give out the conclusion of the advantages and disadvantages of each method, then demonstrated the challenges we face and the possible way to deal with them.
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Chen, Zhe, Zhen Zhang, Fengzhao Dai, Yang Bu, and Huibin Wang. "Monocular Vision-Based Underwater Object Detection." Sensors 17, no. 8 (August 3, 2017): 1784. http://dx.doi.org/10.3390/s17081784.
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Nersesov, B. A. "METHODS OF PROBABILISTIC PERFORMANCE EVALUATION MARINE MAGNETOMETRY." Journal of Oceanological Research 47, no. 4 (December 2, 2019): 152–60. http://dx.doi.org/10.29006/1564-2291.jor-2019.47(4).10.
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Marine magnetometers are promising means of monitoring the water area with an estimated presence of potentially dangerous underwater objects. They are successfully used when searching for underwater objects in conditions of the ineffectiveness of sonar tools: in shallow water, in any media (water, soil) and, especially, at the boundaries of these media. As a rule, the search for an underwater object using magnetometric means is carried out along the “orthogonal tacks” trajectory, the main characteristics of which are the search bandwidth (depending on the magnetic characteristics of the underwater object) and the length of a searching tack. An analysis of the development trends of marine magnetometers revealed the following main directions: the expansion of their functional capabilities, the increase in the sensitivity of magnetic field sensors, and the increase in mass and dimensional characteristics. However, in accordance with modern requirements, the main directions of development of marine magnetometry tools have changed radically. It has been established that the process of detecting an underwater ferromagnetic object by mobile magnetometers is stochastic in nature, which is not taken into account by the traditional method of determining the recommended search band for an underwater object, which leads to the risk of missing it. Therefore, the actual problem of improving search magnetometric means is the development of a methodological apparatus for evaluating the efficiency of detection of underwater objects. A new (probabilistic) approach to the information processing algorithm of the magnetometer signal is proposed, which determines the width of the recommended search band of an object with guaranteed values of the probability characteristics of its detection. The article indicates a problem.
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Zhao, Shijia, Jiachun Zheng, Shidan Sun, and Lei Zhang. "An Improved YOLO Algorithm for Fast and Accurate Underwater Object Detection." Symmetry 14, no. 8 (August 11, 2022): 1669. http://dx.doi.org/10.3390/sym14081669.
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Due to the abundant natural resources of the underwater world, autonomous exploration using underwater robots has become an effective technological tool in recent years. Real-time object detection is critical when employing robots for independent underwater exploration. However, when a robot detects underwater, its computing power is usually limited, which makes it challenging to detect objects effectively. To solve this problem, this study presents a novel algorithm for underwater object detection based on YOLOv4-tiny to achieve better performance with less computational cost. First, a symmetrical bottleneck-type structure is introduced into the YOLOv4-tiny’s backbone network based on dilated convolution and 1 × 1 convolution. It captures contextual information in feature maps with reasonable computational cost and improves the mAP score by 8.74% compared to YOLOv4-tiny. Second, inspired by the convolutional block attention module, a symmetric FPN-Attention module is constructed by integrating the channel-attention module and the spatial-attention module. Features extracted by the backbone network can be fused more efficiently by the symmetric FPN-Attention module, achieving a performance improvement of 8.75% as measured by mAP score compared to YOLOv4-tiny. Finally, this work proposed the YOLO-UOD for underwater object detection through the fusion of the YOLOv4-tiny structure, symmetric FPN-Attention module, symmetric bottleneck-type dilated convolutional layers, and label smoothing training strategy. It can efficiently detect underwater objects in an embedded system environment with limited computing power. Experiments show that the proposed YOLO-UOD outperforms the baseline model on the Brackish underwater dataset, with a detection mAP of 87.88%, 10.5% higher than that of YOLOv4-tiny’s 77.38%, and the detection result exceeds YOLOv5s’s 83.05% and YOLOv5m’s 84.34%. YOLO-UOD is deployed on the embedded system Jetson Nano 2 GB with a detection speed of 9.24 FPS, which shows that it can detect effectively in scenarios with limited computing power.
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Jia, Jiaqi, Min Fu, Xuefeng Liu, and Bing Zheng. "Underwater Object Detection Based on Improved EfficientDet." Remote Sensing 14, no. 18 (September 8, 2022): 4487. http://dx.doi.org/10.3390/rs14184487.
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Intelligent detection of marine organism plays an important part in the marine economy, and it is significant to detect marine organisms quickly and accurately in a complex marine environment for the intelligence of marine equipment. The existing object detection models do not work well underwater. This paper improves the structure of EfficientDet detector and proposes the EfficientDet-Revised (EDR), which is a new marine organism object detection model. Specifically, the MBConvBlock is reconstructed by adding the Channel Shuffle module to enable the exchange of information between the channels of the feature layer. The fully connected layer of the attention module is removed and convolution is used to cut down the amount of network parameters. The Enhanced Feature Extraction module is constructed for multi-scale feature fusion to enhance the feature extraction ability of the network to different objects. The results of experiments demonstrate that the mean average precision (mAP) of the proposed method reaches 91.67% and 92.81% on the URPC dataset and the Kaggle dataset, respectively, which is better than other object detection models. At the same time, the processing speed reaches 37.5 frame per second (FPS) on the URPC dataset, which can meet the real-time requirements. It can provide a useful reference for underwater robots to perform tasks such as intelligent grasping.
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Xiao, Taowen, Zijian Cai, Cong Lin, and Qiong Chen. "A Shadow Capture Deep Neural Network for Underwater Forward-Looking Sonar Image Detection." Mobile Information Systems 2021 (December 30, 2021): 1–10. http://dx.doi.org/10.1155/2021/3168464.
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Image sonar is a widely used wireless communication technology for detecting underwater objects, but the detection process often leads to increased difficulty in object identification due to the lack of equipment resolution. In view of the remarkable results achieved by artificial intelligence techniques in the field of underwater wireless communication research, we propose an object detection method based on convolutional neural network (CNN) and shadow information capture to improve the object recognition and localization effect of underwater sonar images by making full use of the shadow information of the object. We design a Shadow Capture Module (SCM) that can capture the shadow information in the feature map and utilize them. SCM is compatible with CNN models that have a small increase in parameters and a certain degree of portability, and it can effectively alleviate the recognition difficulties caused by the lack of device resolution through referencing shadow features. Through extensive experiments on the underwater sonar data set provided by Pengcheng Lab, the proposed method can effectively improve the feature representation of the CNN model and enhance the difference between class and class features. Under the main evaluation standard of PASCAL VOC 2012, the proposed method improved from an average accuracy (mAP) of 69.61% to 75.73% at an IOU threshold of 0.7, which exceeds many existing conventional deep learning models, while the lightweight design of our proposed module is more helpful for the implementation of artificial intelligence technology in the field of underwater wireless communication.
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Weng, Li Yuan, Min Li, and Zhen Bang Gong. "On Sonar Image Processing Techniques for Detection and Localization of Underwater Objects." Applied Mechanics and Materials 236-237 (November 2012): 509–14. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.509.
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This paper presents an underwater object detection and localization system based on multi-beam sonar image processing techniques. Firstly, sonar data flow collected by multi-beam sonar is processed by median filter to reduce noise. Secondly, an improved adaptive thresholding method based on Otsu method is proposed to extract foreground objects from sonar image. Finally, the object’s contour is calculated by Moore-Neighbor Tracing algorithm to locate the object. Experiments show that the proposed system can detect underwater objects quickly and the figure out the position of objects accurately.
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Zhu, Aoqi, Chen Wang, and Shuai Chen. "Underwater biological detection based on improved YOLOX." Journal of Physics: Conference Series 2405, no. 1 (December 1, 2022): 012010. http://dx.doi.org/10.1088/1742-6596/2405/1/012010.
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Abstract China has abundant Marine resources. Underwater biological detection is important for the development of underwater resources in China. The detection accuracy of underwater biological detection is not high due to poor underwater visibility and the dense existence of biological and complex background information. This paper proposes an improved algorithm of underwater biological detection based on the YOLOX algorithm. CBAM attention mechanism is integrated into the CSPDarkNet of YOLOX, the background and object are distinguished by weighting object information. Considering the detection accuracy and positioning accuracy, the bounding box loss IoU is replaced by DIoU. In the present underwater biological data set, experimental findings demonstrate that the improved algorithm’s detection accuracy reaches 84.76%, which is increased by 2.22%.
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Nersesov, B. A., and N. A. Rimsky-Korsakov. "STATISTICAL SUBSTANTIATION OF THE PERMISSIBLE DECREASE IN THE SENSITIVITY OF THE MAGNETOMETER WITH SAVING THE REQUIRED PROBABILITY OF DETECTING THE UNDERWATER OBJECT." Journal of Oceanological Research 50, no. 2 (August 29, 2022): 178–87. http://dx.doi.org/10.29006/1564-2291.jor-2022.50(2).9.
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The search for underwater potentially dangerous objects using towed magnetometric systems during environmental surveys and rescue operations does not lose its relevance. The existing practice of increasing their effectiveness by increasing the sensitivity of the magnetic field sensor (up to 0.01–0.0001 nT) entailed an increase in weight and size characteristics and cost. At the same time, the stochastic nature of the search process was not taken into account, leading to an indefinite signal-to-noise ratio associated with the random position of an underwater object in the search band, which led to its unacceptable omission. Therefore, the modern search for potentially dangerous underwater objects required their guaranteed detection with an increased probability. The developed methodology for using data from statistical analysis of the amplitude characteristics of the magnetic field induction of an underwater object (signal) and a towing vehicle (interference) makes it possible to determine the width of the guaranteed detection band. The values of the ratios of the average values of the amplitudes of their magnetometric signals, corresponding to the probabilistic characteristics of detection, depending on the values of the magnetic moments in the search band, are determined. Boundary conditions for zones of guaranteed detection of an underwater object with an increased probability Pc = 0.8–1.0 are established in accordance with the level of "signal-to-interference" ratios. Taking into account the stochastic nature of the process of searching for an underwater object, a substantiation of the permissible sensitivity of the magnetometer was carried out, which leads to a decrease in its weight, size and cost characteristics without reducing the required detection probability.
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Zhu, Jianjiang, Siquan Yu, Zhi Han, Yandong Tang, and Chengdong Wu. "Underwater Object Recognition Using Transformable Template Matching Based on Prior Knowledge." Mathematical Problems in Engineering 2019 (February 3, 2019): 1–11. http://dx.doi.org/10.1155/2019/2892975.
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Underwater object recognition in sonar images, such as mine detection and wreckage detection of a submerged airplane, is a very challenging task. The main difficulties include but are not limited to object rotation, confusion from false targets and complex backgrounds, and extensibility of recognition ability on diverse types of objects. In this paper, we propose an underwater object detection and recognition method using a transformable template matching approach based on prior knowledge. Specifically, we first extract features and construct a template from sonar video sequences based on the analysis of acoustic shadows and highlight regions. Then, we identify the target region in the objective image by fast saliency detection techniques based on FFT, which can significantly improve efficiency by avoiding an exhaustive global search. After affine transformation of the template according to the orientation of the target, we extract normalized gradient features and calculate the similarity between the template and the target region, which can solve various difficulties mentioned above using only one template. Experimental results demonstrate that the proposed method can well recognize different underwater objects, such as mine-like objects and triangle-like objects and can satisfy the demands of real-time application.
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Cai, Sixian, Guocheng Li, and Yuan Shan. "Underwater object detection using collaborative weakly supervision." Computers and Electrical Engineering 102 (September 2022): 108159. http://dx.doi.org/10.1016/j.compeleceng.2022.108159.
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Lin Wu, Xin Tian, Jie Ma, and Jinwen Tian. "Underwater Object Detection Based on Gravity Gradient." IEEE Geoscience and Remote Sensing Letters 7, no. 2 (April 2010): 362–65. http://dx.doi.org/10.1109/lgrs.2009.2035455.
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Li, Mengdi, Anumol Mathai, Stephen L. H. Lau, Jian Wei Yam, Xiping Xu, and Xin Wang. "Underwater Object Detection and Reconstruction Based on Active Single-Pixel Imaging and Super-Resolution Convolutional Neural Network." Sensors 21, no. 1 (January 5, 2021): 313. http://dx.doi.org/10.3390/s21010313.
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Due to medium scattering, absorption, and complex light interactions, capturing objects from the underwater environment has always been a difficult task. Single-pixel imaging (SPI) is an efficient imaging approach that can obtain spatial object information under low-light conditions. In this paper, we propose a single-pixel object inspection system for the underwater environment based on compressive sensing super-resolution convolutional neural network (CS-SRCNN). With the CS-SRCNN algorithm, image reconstruction can be achieved with 30% of the total pixels in the image. We also investigate the impact of compression ratios on underwater object SPI reconstruction performance. In addition, we analyzed the effect of peak signal to noise ratio (PSNR) and structural similarity index (SSIM) to determine the image quality of the reconstructed image. Our work is compared to the SPI system and SRCNN method to demonstrate its efficiency in capturing object results from an underwater environment. The PSNR and SSIM of the proposed method have increased to 35.44% and 73.07%, respectively. This work provides new insight into SPI applications and creates a better alternative for underwater optical object imaging to achieve good quality.
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Li, Peng, Yibing Fan, Zhengyang Cai, Zhiyu Lyu, and Weijie Ren. "Detection Method of Marine Biological Objects Based on Image Enhancement and Improved YOLOv5S." Journal of Marine Science and Engineering 10, no. 10 (October 16, 2022): 1503. http://dx.doi.org/10.3390/jmse10101503.
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Marine biological object detection is of great significance for the exploration and protection of underwater resources. There have been some achievements in visual inspection for specific objects based on machine learning. However, owing to the complex imaging environment, some problems, such as low accuracy and poor real-time performance, have appeared in these object detection methods. To solve these problems, this paper proposes a detection method of marine biological objects based on image enhancement and YOLOv5S. Contrast-limited adaptive histogram equalization is taken to solve the problems of underwater image distortion and blur, and we put forward an improved YOLOv5S to improve accuracy and real-time performance of object detection. Compared with YOLOv5S, coordinate attention and adaptive spatial feature fusion are added in the improved YOLOv5S, which can accurately locate the target of interest and fully fuse the features of different scales. In addition, soft non-maximum suppression is adopted to replace non-maximum suppression for the improvement of the detection ability for overlapping objects. The experimental results show that the contrast-limited adaptive histogram equalization algorithm can effectively improve the underwater image quality and the detection accuracy. Compared with the original model (YOLOv5S), the proposed algorithm has a higher detection accuracy. The detection accuracy AP50 reaches 94.9% and the detection speed is 82 frames per second; therefore, the real-time performance can be said to reach a high level.
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Naseer, Atif, Enrique Nava Baro, Sultan Daud Khan, and Yolanda Vila. "A Novel Detection Refinement Technique for Accurate Identification of Nephrops norvegicus Burrows in Underwater Imagery." Sensors 22, no. 12 (June 12, 2022): 4441. http://dx.doi.org/10.3390/s22124441.
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With the evolution of the convolutional neural network (CNN), object detection in the underwater environment has gained a lot of attention. However, due to the complex nature of the underwater environment, generic CNN-based object detectors still face challenges in underwater object detection. These challenges include image blurring, texture distortion, color shift, and scale variation, which result in low precision and recall rates. To tackle this challenge, we propose a detection refinement algorithm based on spatial–temporal analysis to improve the performance of generic detectors by suppressing the false positives and recovering the missed detections in underwater videos. In the proposed work, we use state-of-the-art deep neural networks such as Inception, ResNet50, and ResNet101 to automatically classify and detect the Norway lobster Nephrops norvegicus burrows from underwater videos. Nephrops is one of the most important commercial species in Northeast Atlantic waters, and it lives in burrow systems that it builds itself on muddy bottoms. To evaluate the performance of proposed framework, we collected the data from the Gulf of Cadiz. From experiment results, we demonstrate that the proposed framework effectively suppresses false positives and recovers missed detections obtained from generic detectors. The mean average precision (mAP) gained a 10% increase with the proposed refinement technique.
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Dinakaran, Ranjith, Li Zhang, Chang-Tsun Li, Ahmed Bouridane, and Richard Jiang. "Robust and Fair Undersea Target Detection with Automated Underwater Vehicles for Biodiversity Data Collection." Remote Sensing 14, no. 15 (August 1, 2022): 3680. http://dx.doi.org/10.3390/rs14153680.
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Undersea/subsea data collection via automated underwater vehicles (AUVs) plays an important role for marine biodiversity research, while it is often much more challenging than the data collection above ground via satellites or AUVs. To enable the automated undersea/subsea data collection system, the AUVs are expected to be able to automatically track the objects of interest through what they can “see” from their mounted underwater cameras, where videos or images could be drastically blurred and degraded in underwater lighting conditions. To solve this challenge, in this work, we propose a cascaded framework by combining a DCGAN (deep convolutional generative adversarial network) with an object detector, i.e., single-shot detector (SSD), named DCGAN+SSD, for the detection of various underwater targets from the mounted camera of an automated underwater vehicle. In our framework, our assumption is that DCGAN can be leveraged to alleviate the impact of underwater conditions and provide the object detector with a better performance for automated AUVs. To optimize the hyperparameters of our models, we applied a particle swarm optimization (PSO)-based strategy to improve the performance of our proposed model. In our experiments, we successfully verified our assumption that the DCGAN+SSD architecture can help improve the object detection toward the undersea conditions and achieve apparently better detection rates over the original SSD detector. Further experiments showed that the PSO-based optimization of our models could further improve the model in object detection toward a more robust and fair performance, making our work a promising solution for tackling the challenges in AUVs.
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Liu, Zheng, Yaoming Zhuang, Pengrun Jia, Chengdong Wu, Hongli Xu, and Zhanlin Liu. "A Novel Underwater Image Enhancement Algorithm and an Improved Underwater Biological Detection Pipeline." Journal of Marine Science and Engineering 10, no. 9 (August 28, 2022): 1204. http://dx.doi.org/10.3390/jmse10091204.
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For aquaculture resource evaluation and ecological environment monitoring, the automatic detection and identification of marine organisms is critical; however, due to the low quality of underwater images and the characteristics of underwater biological detection, the lack of abundant features can impede traditional hand-designed feature extraction approaches or CNN-based object detection algorithms, particularly in complex underwater environments. Therefore, the goal of this study was to perform object detection in underwater environments. This study developed a novel method for capturing feature information by adding the convolutional block attention module (CBAM) to the YOLOv5 backbone network. The interference of underwater organism characteristics in object characteristics decreased and the output object information of the backbone network was enhanced. In addition, a self-adaptive global histogram stretching algorithm (SAGHS) was designed to eliminate degradation problems, such as low contrast and color loss, that are caused by underwater environmental features in order to restore image quality. Extensive experiments and comprehensive evaluations using the URPC2021 benchmark dataset demonstrated the effectiveness and adaptivity of the proposed methods. Additionally, this study conducted an exhaustive analysis of the impacts of training data on performance.
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Zhang, Minghua, Shubo Xu, Wei Song, Qi He, and Quanmiao Wei. "Lightweight Underwater Object Detection Based on YOLO v4 and Multi-Scale Attentional Feature Fusion." Remote Sensing 13, no. 22 (November 21, 2021): 4706. http://dx.doi.org/10.3390/rs13224706.
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A challenging and attractive task in computer vision is underwater object detection. Although object detection techniques have achieved good performance in general datasets, problems of low visibility and color bias in the complex underwater environment have led to generally poor image quality; besides this, problems with small targets and target aggregation have led to less extractable information, which makes it difficult to achieve satisfactory results. In past research of underwater object detection based on deep learning, most studies have mainly focused on improving detection accuracy by using large networks; the problem of marine underwater lightweight object detection has rarely gotten attention, which has resulted in a large model size and slow detection speed; as such the application of object detection technologies under marine environments needs better real-time and lightweight performance. In view of this, a lightweight underwater object detection method based on the MobileNet v2, You Only Look Once (YOLO) v4 algorithm and attentional feature fusion has been proposed to address this problem, to produce a harmonious balance between accuracy and speediness for target detection in marine environments. In our work, a combination of MobileNet v2 and depth-wise separable convolution is proposed to reduce the number of model parameters and the size of the model. The Modified Attentional Feature Fusion (AFFM) module aims to better fuse semantic and scale-inconsistent features and to improve accuracy. Experiments indicate that the proposed method obtained a mean average precision (mAP) of 81.67% and 92.65% on the PASCAL VOC dataset and the brackish dataset, respectively, and reached a processing speed of 44.22 frame per second (FPS) on the brackish dataset. Moreover, the number of model parameters and the model size were compressed to 16.76% and 19.53% of YOLO v4, respectively, which achieved a good tradeoff between time and accuracy for underwater object detection.
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Zhang, Xueting, Xiaohai Fang, Mian Pan, Luhua Yuan, Yaxin Zhang, Mengyi Yuan, Shuaishuai Lv, and Haibin Yu. "A Marine Organism Detection Framework Based on the Joint Optimization of Image Enhancement and Object Detection." Sensors 21, no. 21 (October 29, 2021): 7205. http://dx.doi.org/10.3390/s21217205.
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Underwater vision-based detection plays an increasingly important role in underwater security, ocean exploration and other fields. Due to the absorption and scattering effects of water on light, as well as the movement of the carrier, underwater images generally have problems such as noise pollution, color cast and motion blur, which seriously affect the performance of underwater vision-based detection. To address these problems, this study proposes an end-to-end marine organism detection framework that can jointly optimize the image enhancement and object detection. The framework uses a two-stage detection network with dynamic intersection over union (IoU) threshold as the backbone and adds an underwater image enhancement module (UIEM) composed of denoising, color correction and deblurring sub-modules to greatly improve the framework’s ability to deal with severely degraded underwater images. Meanwhile, a self-built dataset is introduced to pre-train the UIEM, so that the training of the entire framework can be performed end-to-end. The experimental results show that compared with the existing end-to-end models applied to marine organism detection, the detection precision of the proposed framework can improve by at least 6%, and the detection speed has not been significantly reduced, so that it can complete the high-precision real-time detection of marine organisms.
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Asyraf, Mohamed Syazwan, Iza Sazanita Isa, Mohd Ikhmal Fitri Marzuki, Siti Noraini Sulaiman, and Chin Chang Hung. "CNN-based YOLOv3 Comparison for Underwater Object Detection." Journal of Electrical & Electronic Systems Research 18, APR2021 (April 1, 2021): 30–37. http://dx.doi.org/10.24191/jeesr.v18i1.005.
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Mathias, Ajisha, Samiappan Dhanalakshmi, R. Kumar, and R. Narayanamoorthi. "Deep Neural Network Driven Automated Underwater Object Detection." Computers, Materials & Continua 70, no. 3 (2022): 5251–67. http://dx.doi.org/10.32604/cmc.2022.021168.
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Gaude, Girish, and Samarth Borkar. "Comprehensive Survey on Underwater Object Detection and Tracking." International Journal of Computer Sciences and Engineering 6, no. 11 (November 30, 2018): 304–13. http://dx.doi.org/10.26438/ijcse/v6i11.304313.
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Zarkasi, Ahmad, Sutarno Sutarno, Huda Ubaya, and Muhammad Fajar. "Implementation Color Filtering and Harris Corner Method on Pattern Recognition System." Computer Engineering and Applications Journal 6, no. 3 (October 14, 2017): 139–44. http://dx.doi.org/10.18495/comengapp.v6i3.219.
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Color recognition and angle detection of underwater objects can be done with the help of underwater robots (ROV) with image processing applications. The processing of the object's image is recognizing various shapes and colors of objects in the water. In this research, the color filtering and Harris corner method will be designed, studied, tested and implemented. The color filtering method is used to recognize object color patterns, while the Harris Corner method is used to detect angles of underwater objects. Then classify images to get data on environmental pattern recognition. The color patterns tested include red, green, yellow and blue. the results obtained are all color patterns can be recognized well. while the shape of the object being tested includes cubes, triangles, rectangles, pentagons, and hexagons. the results of testing some of the shapes can be detected with a good angle and others still have errors. This is because testing the form of objects is done in various positions, such as from the front, right, left, up and below.Â
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FORESTI, G. L., and S. GENTILI. "A VISION BASED SYSTEM FOR OBJECT DETECTION IN UNDERWATER IMAGES." International Journal of Pattern Recognition and Artificial Intelligence 14, no. 02 (March 2000): 167–88. http://dx.doi.org/10.1142/s021800140000012x.
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In this paper, a vision-based system for underwater object detection is presented. The system is able to detect automatically a pipeline placed on the sea bottom, and some objects, e.g. trestles and anodes, placed in its neighborhoods. A color compensation procedure has been introduced in order to reduce problems connected with the light attenuation in the water. Artificial neural networks are then applied in order to classify in real-time the pixels of the input image into different classes, corresponding e.g. to different objects present in the observed scene. Geometric reasoning is applied to reduce the detection of false objects and to improve the accuracy of true detected objects. The results on real underwater images representing a pipeline structure in different scenarios are shown. The presence of seaweed and sand, different illumination conditions and water depth, different pipeline diameter and small variations of the camera tilt angle are considered to evaluate the algorithm performances.
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Ma, Chao, Chun Jie Qiao, Yue Ke Wang, and Shen Zhao. "A Real-Time Processing Method of Underwater Ambient Noise Spectrum Based on One-Third Octave." Applied Mechanics and Materials 333-335 (July 2013): 522–25. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.522.
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Extended, continuous measuring and analysis for underwater ambient noise can find the objects like ships and submarine. It is important for passive detection of underwater object. A real-time processing method of one-third octave spectrum is presented. It is shown by tests that the method is steady and satisfy the real-time processing.
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Liu, Tao, Shuangyan He, Haoyang Liu, Yanzhen Gu, and Peiliang Li. "A Robust Underwater Multiclass Fish-School Tracking Algorithm." Remote Sensing 14, no. 16 (August 21, 2022): 4106. http://dx.doi.org/10.3390/rs14164106.
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State-of-the-art multiple-object tracking methods are frequently applied to people or vehicle tracking, but rarely involve underwater-object tracking. Compared with the processing in non-underwater photos or videos, underwater fish tracking is challenging due to variations in light conditions, water turbidity levels, shape deformations, and the similar appearances of fish. This article proposes a robust underwater fish-school tracking algorithm (FSTA). The FSTA is based on the tracking-by-detection paradigm. To solve the problem of low recognition accuracy in an underwater environment, we add an amendment detection module that uses prior knowledge to modify the detection result. Second, we introduce an underwater data association algorithm for aquatic non-rigid organisms that recombines representation and location information to refine the data matching process and improve the tracking results. The Resnet50-IBN network is used as a re-identification network to track fish. We introduce a triplet loss function based on a centroid to train the feature extraction network. The multiple-object tracking accuracy (MOTA) of the FSTA is 79.1% on the underwater dataset, which shows that it can achieve state-of-the-art performance in a complex real-world marine environment.
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Ge, Huilin, Yuewei Dai, Zhiyu Zhu, and Xu Zang. "Single-Stage Underwater Target Detection Based on Feature Anchor Frame Double Optimization Network." Sensors 22, no. 20 (October 17, 2022): 7875. http://dx.doi.org/10.3390/s22207875.
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Objective: The shallow underwater environment is complex, with problems of color shift, uneven illumination, blurring, and distortion in the imaging process. These scenes are very unfavorable for the reasoning of the detection network. Additionally, typical object identification algorithms struggle to maintain high resilience in underwater environments due to picture domain offset, making underwater object detection problematic. Methods: This paper proposes a single-stage detection method with the double enhancement of anchor boxes and features. The feature context relevance is improved by proposing a composite-connected backbone network. The receptive field enhancement module is introduced to enhance the multi-scale detection capability. Finally, a prediction refinement strategy is proposed, which refines the anchor frame and features through two regressions, solves the problem of feature anchor frame misalignment, and improves the detection performance of the single-stage underwater algorithm. Results: We achieved an effect of 80.2 mAP on the Labeled Fish in the Wild dataset, which saves some computational resources and time while still improving accuracy. On the original basis, UWNet can achieve 2.1 AP accuracy improvement due to the powerful feature extraction function and the critical role of multi-scale functional modules. At an input resolution of 300 × 300, UWNet can provide an accuracy of 32.4 AP. When choosing the number of prediction layers, the accuracy of the four and six prediction layer structures is compared. The experiments show that on the Labeled Fish in the Wild dataset, the six prediction layers are better than the four. Conclusion: The single-stage underwater detection model UWNet proposed in this research has a double anchor frame and feature optimization. By adding three functional modules, the underwater detection of the single-stage detector is enhanced to address the issue that it is simple to miss detection while detecting small underwater targets.
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Nguyen, Huu Thu, Eon-Ho Lee, Chul Hee Bae, and Sejin Lee. "Multiple Object Detection Based on Clustering and Deep Learning Methods." Sensors 20, no. 16 (August 7, 2020): 4424. http://dx.doi.org/10.3390/s20164424.
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Multiple object detection is challenging yet crucial in computer vision. In This study, owing to the negative effect of noise on multiple object detection, two clustering algorithms are used on both underwater sonar images and three-dimensional point cloud LiDAR data to study and improve the performance result. The outputs from using deep learning methods on both types of data are treated with K-Means clustering and density-based spatial clustering of applications with noise (DBSCAN) algorithms to remove outliers, detect and cluster meaningful data, and improve the result of multiple object detections. Results indicate the potential application of the proposed method in the fields of object detection, autonomous driving system, and so forth.
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Razali, Raiz, Muhammad Syafiq Jasmee, and Nafisah Khalid. "Comparative Analysis between Bathymetry and Backscatter Underwater Object Detection." IOP Conference Series: Earth and Environmental Science 767, no. 1 (May 1, 2021): 012035. http://dx.doi.org/10.1088/1755-1315/767/1/012035.
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Wu, Lin, and Jinwen Tian. "Automated gravity gradient tensor inversion for underwater object detection." Journal of Geophysics and Engineering 7, no. 4 (October 26, 2010): 410–16. http://dx.doi.org/10.1088/1742-2132/7/4/008.
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Jeon, MyungHwan, Yeongjun Lee, Young-Sik Shin, Hyesu Jang, and Ayoung Kim. "Underwater Object Detection and Pose Estimation using Deep Learning." IFAC-PapersOnLine 52, no. 21 (2019): 78–81. http://dx.doi.org/10.1016/j.ifacol.2019.12.286.
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Wang, Jun, Shuman Qi, Chao Wang, Jin Luo, Xin Wen, and Rui Cao. "B-YOLOX-S: A Lightweight Method for Underwater Object Detection Based on Data Augmentation and Multiscale Feature Fusion." Journal of Marine Science and Engineering 10, no. 11 (November 16, 2022): 1764. http://dx.doi.org/10.3390/jmse10111764.
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With the increasing maturity of underwater agents-related technologies, underwater object recognition algorithms based on underwater robots have become a current hotspot for academic and applied research. However, the existing underwater imaging conditions are poor, the images are blurry, and the underwater robot visual jitter and other factors lead to lower recognition precision and inaccurate positioning in underwater target detection. A YOLOX-based underwater object detection model, B-YOLOX-S, is proposed to detect marine organisms such as echinus, holothurians, starfish, and scallops. First, Poisson fusion is used for data amplification at the input to balance the number of detected targets. Then, wavelet transform is used to perform Style Transfer on the enhanced images to achieve image restoration. The clarity of the images and detection targets is further increased and the generalization of the model is enhanced. Second, a combination of BIFPN-S and FPN is proposed to fuse the effective feature layer obtained by the Backbone layer to enhance the detection precision and accelerate model detection. Finally, the localization loss function of the prediction layer in the network is replaced by EIoU_Loss to heighten the localization precision in detection. Experimental results comparing the B-YOLOX-S algorithm model with mainstream algorithms such as FasterRCNN, YOLOV3, YOLOV4, YOLOV5, and YOLOX on the URPC2020 dataset show that the detection precision and detection speed of the algorithm model have obvious advantages over other algorithm networks. The average detection accuracy mAP value is 82.69%, which is 5.05% higher than the benchmark model (YOLOX-s), and the recall rate is 8.03% higher. Thus, the validity of the algorithmic model proposed in this paper is demonstrated.
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Himri, Khadidja, Pere Ridao, and Nuno Gracias. "Underwater Object Recognition Using Point-Features, Bayesian Estimation and Semantic Information." Sensors 21, no. 5 (March 5, 2021): 1807. http://dx.doi.org/10.3390/s21051807.
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This paper proposes a 3D object recognition method for non-coloured point clouds using point features. The method is intended for application scenarios such as Inspection, Maintenance and Repair (IMR) of industrial sub-sea structures composed of pipes and connecting objects (such as valves, elbows and R-Tee connectors). The recognition algorithm uses a database of partial views of the objects, stored as point clouds, which is available a priori. The recognition pipeline has 5 stages: (1) Plane segmentation, (2) Pipe detection, (3) Semantic Object-segmentation and detection, (4) Feature based Object Recognition and (5) Bayesian estimation. To apply the Bayesian estimation, an object tracking method based on a new Interdistance Joint Compatibility Branch and Bound (IJCBB) algorithm is proposed. The paper studies the recognition performance depending on: (1) the point feature descriptor used, (2) the use (or not) of Bayesian estimation and (3) the inclusion of semantic information about the objects connections. The methods are tested using an experimental dataset containing laser scans and Autonomous Underwater Vehicle (AUV) navigation data. The best results are obtained using the Clustered Viewpoint Feature Histogram (CVFH) descriptor, achieving recognition rates of 51.2%, 68.6% and 90%, respectively, clearly showing the advantages of using the Bayesian estimation (18% increase) and the inclusion of semantic information (21% further increase).
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Cai, Liangwei, Ceng Wang, and Yuan Xu. "A Real-Time FPGA Accelerator Based on Winograd Algorithm for Underwater Object Detection." Electronics 10, no. 23 (November 23, 2021): 2889. http://dx.doi.org/10.3390/electronics10232889.
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Real-time object detection is a challenging but crucial task for autonomous underwater vehicles because of the complex underwater imaging environment. Resulted by suspended particles scattering and wavelength-dependent light attenuation, underwater images are always hazy and color-distorted. To overcome the difficulties caused by these problems to underwater object detection, an end-to-end CNN network combined U-Net and MobileNetV3-SSDLite is proposed. Furthermore, the FPGA implementation of various convolution in the proposed network is optimized based on the Winograd algorithm. An efficient upsampling engine is presented, and the FPGA implementation of squeeze-and-excitation module in MobileNetV3 is optimized. The accelerator is implemented on a Zynq XC7Z045 device running at 150 MHz and achieves 23.68 frames per second (fps) and 33.14 fps when using MobileNetV3-Large and MobileNetV3-Small as the feature extractor. Compared to CPU, our accelerator achieves 7.5×–8.7× speedup and 52×–60× energy efficiency.
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Wang, Lin, Xiufen Ye, Shunli Wang, and Peng Li. "ULO: An Underwater Light-Weight Object Detector for Edge Computing." Machines 10, no. 8 (July 29, 2022): 629. http://dx.doi.org/10.3390/machines10080629.
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Recent studies on underwater object detection have progressed with the development of deep-learning methods. Generally, the model performance increase is accompanied by an increase in computation. However, a significant fraction of remotely operated underwater vehicles (ROVs) and autonomous underwater vehicles (AUVs) operate in environments with limited power and computation resources, making large models inapplicable. In this paper, we propose a fast and compact object detector—namely, the Underwater Light-weight Object detector (ULO)—for several marine products, such as scallops, starfish, echinus, and holothurians. ULO achieves comparable results to YOLO-v3 with less than 7% of its computation. ULO is modified based on the YOLO Nano architecture, and some modern architectures are used to optimize it, such as the Ghost module and decoupled head design in detection. We propose an adaptive pre-processing module for the image degradation problem that is common in underwater images. The module is lightweight and simple to use, and ablation experiments verify its effectiveness. Moreover, ULO Tiny, a lite version of ULO, is proposed to achieve further computation reduction. Furthermore, we optimize the annotations of the URPC2019 dataset, and the modified annotations are more accurate in localization and classification. The refined annotations are available to the public for research use.
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Foresti, Gian Luca, and Ivan Scagnetto. "An integrated low-cost system for object detection in underwater environments." Integrated Computer-Aided Engineering 29, no. 2 (March 14, 2022): 123–39. http://dx.doi.org/10.3233/ica-220675.
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We propose a novel low-cost integrated system prototype able to recognize objects/lifeforms in underwater environments. The system has been applied to detect unexploded ordnance materials in shallow waters. Indeed, small and agile remotely controlled vehicles with cameras can be used to detect unexploded bombs in shallow waters, more effectively and freely than complex, costly and heavy equipment, requiring several human operators and support boats. Moreover, visual techniques can be easily combined with the traditional use of magnetometers and scanning imaging sonars, to improve the effectiveness of the survey. The proposed system can be easily adapted to other scenarios (e.g., underwater archeology or visual inspection of underwater pipelines and implants), by simply replacing the Convolutional Neural Network devoted to the visual identification task. As a final outcome of our work we provide a large dataset of images of explosive materials: it can be used to compare different visual techniques on a common basis.
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Matharu, Pawandeep Singh, Akash Ashok Ghadge, Yara Almubarak, and Yonas Tadesse. "Jelly-Z: Twisted and coiled polymer muscle actuated jellyfish robot for environmental monitoring." ACTA IMEKO 11, no. 3 (September 5, 2022): 1. http://dx.doi.org/10.21014/acta_imeko.v11i3.1255.
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Silent underwater actuation and object detection are desired for certain applications in environmental monitoring. However, several challenges need to be faced when addressing simultaneously the issues of actuation and object detection using vision system. This paper presents a swimming underwater soft robot inspired by the moon jellyfish (Aurelia aurita) species and other similar robots; however, this robot uniquely utilizes novel artificial muscles and incorporates camera for visual information processing. The actuation characteristics of the novel artificial muscles in water are presented which can be used for any other applications. The bio-inspired robot, Jelly-Z, has the following characteristics: (1) The integration of three 60 mm-long twisted, and coiled polymer fishing line (TCPFL) muscles in a silicone bell to achieve contraction and expansion motions for swimming; (2) A Jevois camera is mounted on Jelly-Z to perform object detection while swimming using a pre-trained neural network; (3) Jelly-Z weighs a total of 215 g with all its components and is capable of swimming 360 mm in 63 seconds. The present work shows, for the first time, the integration of camera detection and TCPFL actuators in an underwater soft jellyfish robot, and the associated performance characteristics. This kind of robot can be a good platform for monitoring of aquatic environment either for detection of objects by estimating the percentage of similarity to pre-trained network or by mounting sensors to monitor water quality when fully developed.