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underwater object detection by fusing features from different representations of sonar data

Frontiers of Information Technology & Electronic Engineering 2023年第6期24卷 828-843页

作者： Fei WANG Wanyu LI Miao LIU Jingchun ZHOU Weishi ZHANG College of Information Science and Technology Dalian Maritime UniversityDalian 116026China College of Transportation Engineering Dalian Maritime UniversityDalian 116026China

Modern underwater object detection methods recognize objects from sonar data based on their geometric shapes.However,the distortion of objects during data acquisition and representation is seldom considered.In this paper,we present a detailed summary of representations for sonar data and a concrete analysis of the geometric characteristics of different data representations.Based on this,a feature fusion framework is proposed to fully use the intensity features extracted from the polar image representation and the geometric features learned from the point cloud representation of sonar data.Three feature fusion strategies are presented to investigate the impact of feature fusion on different components of the detection pipeline.In addition,the fusion strategies can be easily integrated into other detectors,such as the You Only Look Once(YOLO)series.The effectiveness of our proposed framework and feature fusion strategies is demonstrated on a public sonar dataset captured in real-world underwater environments.Experimental results show that our method benefits both the region proposal and the object classification modules in the detectors.

关键词： underwater object detection Sonar data representation Feature fusion

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An underwater Target detection Algorithm Based on Attention Mechanism and Improved YOLOv7

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Computers, Materials & Continua 2024年第2期78卷 2829-2845页

作者： Liqiu Ren Zhanying Li Xueyu He Lingyan Kong Yinghao Zhang College of Information Science and Engineering Dalian Polytechnic UniversityDalian116034China

For underwater robots in the process of performing target detection tasks,the color distortion and the uneven quality of underwater images lead to great difficulties in the feature extraction process of the model,which is prone to issues like error detection,omission detection,and poor accuracy.Therefore,this paper proposed the CER-YOLOv7(CBAM-EIOU-RepVGG-YOLOv7)underwater target detection algorithm.To improve the algorithm’s capability to retain valid features from both spatial and channel perspectives during the feature extraction phase,we have added a Convolutional Block Attention Module(CBAM)to the backbone network.The Reparameterization Visual Geometry Group(RepVGG)module is inserted into the backbone to improve the training and inference capabilities.The Efficient Intersection over Union(EIoU)loss is also used as the localization loss function,which reduces the error detection rate and missed detection rate of the algorithm.The experimental results of the CER-YOLOv7 algorithm on the UPRC(underwater Robot Prototype Competition)dataset show that the mAP(mean Average Precision)score of the algorithm is 86.1%,which is a 2.2%improvement compared to the YOLOv7.The feasibility and validity of the CER-YOLOv7 are proved through ablation and comparison experiments,and it is more suitable for underwater target detection.

关键词： Deep learning underwater object detection improved YOLOv7 attention mechanism

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Review of Development of Multi-static Sonar for underwater object detection

Review of Development of Multi-static Sonar for Underwater O...

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2012年计算机应用与系统建模国际会议

作者： Yang Guang Li Ye Xiang Xu Wang Zhognqiu Shandong Provincial Key Laboratory of Ocean Environment Monitoring Technology Shandong Academy of Sciences Institute of Oceanographic Instrumentation

In recent years, as the increasing requirement of submarine detection, the multi-static sonar technique is more and more investigated. Multi-static sonar has become one of the main techniques for countermeasure of silent submarine because of the advantages, such as large detection area, good occultation, flexible configuration, and strong ability of antidisturbance. In this paper, the state of the art of multi-static sonar research work is reviewed, and the representative positioning algorithms for multi-static sonar are summarized from multi-static sonar equation, that may be useful as reference of researchers.

关键词： Multi-static sonar Acoustic underwater object detection

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Analytical review and study on object detection techniques in the image

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International Journal of Modeling, Simulation, and Scientific Computing 2021年第5期12卷 1-19页

作者： Sriram K.V R.H.Havaldar Electronics and Communication Engineering Angadi Institute of Technology and Management Belagavi KarnatakaIndia Biomedical Engineering KLE Dr.M.S.Sheshgiri College of Engineering and Technology BelagaviKarnatakaIndia

object detection is the most fundamental but challenging issues in the field of computer vision.object detection identifies the presence of various individual objects in an image.Great success is attained for object detection/recognition problems in the controlled environment,but still,the problem remains unsolved in the uncontrolled places,particularly,when the objects are placed in arbitrary poses in an occluded and cluttered environment.In the last few years,a lots of efforts are made by researchers to resolve this issue,because of its wide range of applications in computer vision tasks,like content-enabled image retrieval,event or activity recognition,scene understanding,and so on.This review provides a detailed survey of 50 research papers presenting the object detection techniques,like machine learning-based techniques,gradient-based techniques,Fast Region-based Convolutional Neural Network(Fast R-CNN)detector,and the foreground-based techniques.Here,the machine learning-based approaches are classified into deep learning-based approaches,random forest,Support Vector Machine(SVM),and so on.Moreover,the challenges faced by the existing techniques are explained in the gaps and issues section.The analysis based on the classification,toolset,datasets utilized,published year,and the performance metrics are discussed.The future dimension of the research is based on the gaps and issues identified from the existing research works.

关键词： object detection fast region-based convolutional neural network foreground object detection underwater object detection mean average precision activity recognition

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