Underwater Target Detection Based on Sonar Image

HAO Zixiao; WANG Qi

doi:10.11993/j.issn.2096-3920.202205004

Volume 31 Issue 2

Apr 2023

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Article Navigation > Journal of Unmanned Undersea Systems > 2023 > 31(2): 339-348

HAO Zixiao, WANG Qi. Underwater Target Detection Based on Sonar Image[J]. Journal of Unmanned Undersea Systems, 2023, 31(2): 339-348. doi: 10.11993/j.issn.2096-3920.202205004

Citation:

HAO Zixiao, WANG Qi. Underwater Target Detection Based on Sonar Image[J]. Journal of Unmanned Undersea Systems, 2023, 31(2): 339-348. doi: 10.11993/j.issn.2096-3920.202205004

HAO Zixiao, WANG Qi. Underwater Target Detection Based on Sonar Image[J]. Journal of Unmanned Undersea Systems, 2023, 31(2): 339-348. doi: 10.11993/j.issn.2096-3920.202205004

Citation:

HAO Zixiao, WANG Qi. Underwater Target Detection Based on Sonar Image[J]. Journal of Unmanned Undersea Systems, 2023, 31(2): 339-348. doi: 10.11993/j.issn.2096-3920.202205004

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Underwater Target Detection Based on Sonar Image

doi: 10.11993/j.issn.2096-3920.202205004

HAO Zixiao,
WANG Qi

School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, China

Received Date: 2022-05-13
Rev Recd Date: 2022-07-28

Available Online: 2022-12-05

Abstract

Abstract

Underwater target detection by processing sonar images is of great military and civil significance. This paper comprehensively describes the principles, methods, algorithms, and development trends in underwater target detection based on sonar images. Initially, we divide the underwater target detection task based on sonar images into traditional, deep learning-based, and combined deep learning- and transfer learning-based underwater target detection. Traditional target detection is divided into underwater target detection based on mathematical statistics, mathematical morphology, and pixels. Deep learning-based target detection methods are primarily divided into one-stage, two-stage, and detection transformer(DETR) methods. Combined deep learning- and transfer learning-based target detection is primarily divided into target detection based on simple deep neural network model transfer and complex deep learning model transfer. Finally, the advantages and disadvantages of the existing technology are summarized, and the future development direction of this field is discussed.
- underwater target detection,
- sonar image,
- deep learning,
- transfer learning

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References(64)

References

[1]	贾宇. 关于海洋强国战略的思考[J]. 太平洋学报, 2018, 26(1): 1-8. doi: 10.14015/j.cnki.1004-8049.2018.01.001 Jia Yu. On China’s maritime power strategy[J]. Pacific Journal, 2018, 26(1): 1-8. doi: 10.14015/j.cnki.1004-8049.2018.01.001
[2]	Bryner D, Huffer F, Srivastava A, et al. Underwater minefield detection in clutter data using spatial point-process models[J]. IEEE Journal of Oceanic Engineering, 2016, 41(3): 670-681. doi: 10.1109/JOE.2015.2493598
[3]	Ceccarini C. The multi-role nato-interoperable light weight torpedo for the 21st century[J]. Information & Security an International Journal, 2004, 13(1): 89-97.
[4]	Chuang M C, Hwang J N, Ye J H, et al. Underwater fish tracking for moving cameras based on deformable multiple kernels[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2016, 47(9): 1-11.
[5]	张韫峰, 李娟, 黎明. 基于图像处理的水下海参识别和定位方法[J]. 水下无人系统学报, 2021, 29(1): 111-123. doi: 10.11993/j.issn.2096-3920.2021.01.016 Zhang Yunfeng, Li Juan, Li Ming. Underwater sea cucumber identification and localization method based on image processing[J]. Journal of Unmanned Undersea Systems, 2021, 29(1): 111-123. doi: 10.11993/j.issn.2096-3920.2021.01.016
[6]	Byun S W, Kim J Y. Development and experiment of a hovering AUV tested-bed for underwater exploration[C]// Symposium on Underwater Technology & Workshop on Scientific Use of Submarine Cables& Related Technologies. Tokyo, Japan: IEEE, 2007.
[7]	李勇航, 牟泽霖, 万芃. 海洋侧扫声呐探测技术的现状及发展[J]. 通讯世界, 2015(3): 213-214. doi: 10.3969/j.issn.1006-4222.2015.03.137
[8]	Hayes M P, Gough P T. Synthetic aperture sonar: A review of current status[J]. IEEE Journal of Oceanic Engineering, 2009, 34(3): 207-224. doi: 10.1109/JOE.2009.2020853
[9]	Komatsu T, Igarashi C, Tatsukawa K, et al. Use of multi-beam sonar to map seagrass beds in otsuchi bay on the Sanriku Coast of Japan[J]. Aquatic Living Resources, 2003, 16(3): 223-230. doi: 10.1016/S0990-7440(03)00045-7
[10]	Lowe D G. Distinctive image features from scale-invariant key points[J]. International Journal of Computer Vision, 2003, 20: 91-110.
[11]	Bay H, Tuytelaars T, Gool L V. SURF: Speeded up robust features[C]//Proceedings of the 9th European Conference on Computer Vision-Volume Part I. Berlin, Heidelberg: Springer-Verlag, 2006.
[12]	Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]//IEEE Computer Society Conference on Computer Vision & Pattern Recognition. San Diego, CA, USA: IEEE, 2005.
[13]	Burges C. A Tutorial on support vector machines for pattern recognition[J]. Data Mining and Knowledge Discovery, 1998, 2(2): 121-167. doi: 10.1023/A:1009715923555
[14]	Rish I. An empirical study of the naive Bayes classifier[J]. Journal of Universal Computer Science, 2001, 1(2): 127.
[15]	Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60: 84-90.
[16]	Lin T Y, Dollar P, Girshick R, et al. Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017: 2117-2125.
[17]	陈强, 田杰, 黄海宁, 等. 基于统计和纹理特征的SAS图像SVM分割研究[J]. 仪器仪表学报, 2013, 34(6): 214-221. doi: 10.3969/j.issn.0254-3087.2013.06.031 Chen Qiang, Tian Jie, Huang Haining, et al. Study on SAS image segmentation using SVM based on statistical and texture features[J]. Chinese Journal of Scientific Instrument, 2013, 34(6): 214-221. doi: 10.3969/j.issn.0254-3087.2013.06.031
[18]	王涛, 潘国富, 张济博. 基于侧扫声呐图像纹理特征的海底底质分类研究[C]//2020中国西部声学学术交流会论文集. 酒泉: 2020中国西部声学学术交流会, 2020: 403-406.
[19]	董凌宇, 单瑞, 刘慧敏, 等. 基于分形纹理特征的侧扫声呐图像沉船识别方法研究[J]. 海洋地质与第四纪地质, 2021, 41(4): 232-239. doi: 10.16562/j.cnki.0256-1492.2020070301 Dong Lingyu, Shan Rui, Liu Huimin, et al. Shipwreck identification with side scan sonar image based on fractal texture[J]. Marine Geology & Quaternary Geology, 2021, 41(4): 232-239. doi: 10.16562/j.cnki.0256-1492.2020070301
[20]	王其林, 王宏健, 李庆, 等. 侧扫声呐图像特征提取改进方法[J]. 水下无人系统学报, 2019, 27(3): 297-304. Wang Qilin, Wang Hongjian, Li Qing, et al. Improved feature extraction method for side scan sonar images[J]. Journal of Unmanned Undersea Systems, 2019, 27(3): 297-304.
[21]	田晓东, 刘忠, 周德超. 基于形状描述直方图的声呐图像目标识别算法[J]. 系统工程与电子技术, 2007(7): 1049-1052,1212. doi: 10.3321/j.issn:1001-506X.2007.07.008 Tian Xiaodong, Liu Zhong, Zhou Dechao. Mine target recognition algorithm of sonar image[J]. Systems Engineering and Electronics, 2007(7): 1049-1052,1212. doi: 10.3321/j.issn:1001-506X.2007.07.008
[22]	卢逢春, 张殿伦, 郭海涛. 基于属性直方图的图像分割方法及其在声呐图像分割中的应用[J]. 哈尔滨工程大学学报, 2002(3): 1-3,19. doi: 10.3969/j.issn.1006-7043.2002.03.001 Lu Fengchun, Zhang Dianlun, Guo Haitao. Image segmentation based upon bounded histogram and its application to sonar image segmentation[J]. Journal of Harbin Engineering University, 2002(3): 1-3,19. doi: 10.3969/j.issn.1006-7043.2002.03.001
[23]	Yang F, Du Z, Wu Z. Object recognizing on sonar image based on histogram and geometric feature[J]. Marine Science Bulletin, 2006, 25(5): 64.
[24]	王晓, 邹泽伟, 李勃勃, 等. 基于多特征融合的彩色图像声呐目标检测[J]. 计算机科学, 2019, 46(S1): 177-181.
[25]	Sun C, Wang L, Wang N, et al. Image recognition technology in texture identification of marine sediment sonar image[J]. Complexity, 2021, 2021(2): 1-8.
[26]	罗进华, 蒋锦朋, 朱培民. 基于数学形态学的侧扫声呐图像轮廓自动提取[J]. 海洋学报, 2016, 38(5): 150-157. Luo Jinhua, Jiang Jinpeng, Zhu Peimin. Automatic extraction of the side-scan sonar imagery outlines based on mathematical morphology[J]. Haiyang Xuebao, 2016, 38(5): 150-157.
[27]	邹岗, 田晓东, 刘忠. 基于几何特征的声呐图像人造目标检测算法[J]. 舰船科学技术, 2007, 29(6): 177-179, 183. Zou Gang, Tian Xiaodong, Liu Zhong. Man-made target detection algorithm of sonar image based on geometric feature[J]. Ship Science and Technology, 2007, 29(6): 177-179, 183.
[28]	Mallet J, Courmontagne P. A new wavelet thresholding approach for SAS images denoising[C]//Oceans, 2006. Boston, USA: IEEE, 2006: 1-6.
[29]	Isar A, Isar D, Moga S, et al. Multi-scale MAP despeckling of sonar images[C]//Oceans 2005-Europe. Brest, France: IEEE, 2005.
[30]	金凤来, 钟何平. 一种改进的合成孔径声呐图像Lee滤波算法[J]. 舰船电子工程, 2017, 37(3): 35-37. doi: 10.3969/j.issn.1672-9730.2017.03.009 Jin Fenglai, Zhong Heping. An improved Lee filter algorithm for synthetic aperture sonar[J]. Ship Electronic Engineering, 2017, 37(3): 35-37. doi: 10.3969/j.issn.1672-9730.2017.03.009
[31]	郭海涛, 徐雷, 赵红叶, 等. 一种抑制声呐图像散斑噪声的形态学滤波器[J]. 仪器仪表学报, 2015, 36(3): 654-660. doi: 10.19650/j.cnki.cjsi.2015.03.022 Guo Haitao, Xu Lei, Zhao Hongye, et al. A morphological filter for despeckling of a sonar image[J]. Chinese Journal of Scientific Instrument, 2015, 36(3): 654-660. doi: 10.19650/j.cnki.cjsi.2015.03.022
[32]	崔杰, 胡长青, 徐海东. 基于帧差法的多波束前视声呐运动目标检测[J]. 仪器仪表学报, 2018, 39(2): 169-176. doi: 10.19650/j.cnki.cjsi.j1702414 Cui Jie, Hu Changqing, Xu Haidong. Moving target detection for multi-beam forward-looking sonar based on frame-difference method[J]. Chinese Journal of Scientific Instrument, 2018, 39(2): 169-176. doi: 10.19650/j.cnki.cjsi.j1702414
[33]	崔杰, 胡长青, 徐海东, 等. 改进的Mean Shift算法在前视声呐运动目标跟踪中的应用[J]. 声学技术, 2020, 39(3): 279-283. Cui Jie, Hu Changqing, Xu Haidong, et al. Application of improved mean shift algorithm in moving target tracking of forward-looking sonar[J]. Technical Acoustics, 2020, 39(3): 279-283.
[34]	Liu H, Dai J, Wang R, et al. Combining background subtraction and three-frame difference to detect moving object from underwater video[C]//Oceans-shanghai. Shanghai, China: IEEE, 2016.
[35]	Kalyan B, Balasuriya A. Sonar based automatic target detection scheme for underwater environments using CFAR techniques: A comparative study[C]//Proceedings of the 2004 International Symposium on Underwater Technology(IEEE Cat. No. 04EX869). Taipei: IEEE, 2005.
[36]	Li K, Liu Z, Hu S L, et al. Target detection of sonar image based on bis-parameter with adaptive windows[J]. Applied Mechanics & Materials, 2013, 347-350: 3407-3410.
[37]	Villar S A, Acosta G G, Solari F J. OS-CFAR process in 2-D for object segmentation from side-scan sonar data[C]//Information Processing & Control. Cordoba, Argentina: IEEE, 2016.
[38]	Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA: IEEE, 2014: 580-587.
[39]	Girshick R. Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015: 1440-1448.
[40]	Ren S, He K, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal net- works[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6): 1137-1149.
[41]	曾文冠, 鲁建华. 基于卷积神经网络的声呐图像目标检测识别[C]//第十七届船舶水下噪声学术讨论会论文集. 衢州, 中国: 《船舶力学》编辑部, 2019.
[42]	Fang J, Wang P. Target detection in sonar image based on Faster RCNN[C]//2020 International Conference on Information Science, Parallel and Distributed Systems(ISPDS). Xi’an, China: IEEE, 2020.
[43]	Ma Q, Jiang L, Yu W, et al. Training with noise adversarial network: A generalization method for object detection on sonar image[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. Snowmass, USA: IEEE, 2020: 729-738.
[44]	马麒翔. 基于深度学习的声呐图像目标检测算法研究[D]. 南京: 东南大学, 2020.
[45]	Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]//NIPS’14: Proceedings of the 27th International Conference on Neural Information Processing Systems. [S.l.]: MIT Press, 2014.
[46]	Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016: 779-788.
[47]	Liu W, Anguelov D, Erhan D, et al. SSD: Single shot multibox detector[C]//Computer Vision ECCV 2016. European Conference on Computer Vision-Amsterdam. Cham: Springer, 2016, 9905: 21-37.
[48]	Wu Y. Sonar image target detection and recognition based on convolution neural network[J]. Mobile Information Systems, 2021, 2021(6): 1-8.
[49]	王霞. 面向声呐图像的水下小目标识别方法研究[D]. 哈尔滨: 哈尔滨工程大学, 2020.
[50]	Fan X, Lu L, Shi P, et al. A novel sonar target detection and classification algorithm[J]. Multimedia Tools and Applications, 2022, 81(7): 10091-10106. doi: 10.1007/s11042-022-12054-4
[51]	Han K, Wang Y, Chen H, et al. A survey on vision transformer[C]//IEEE Transactions on Pattern Analysis and Machine Intelligence. Piscataway, USA: IEEE, 2022.
[52]	Yu Y, Zhao J, Gong Q, et al. Real-time underwater maritime object detection in side-scan sonar images based on transformer-YOLOV5[J]. Remote Sensing, 2021, 13(18): 3555. doi: 10.3390/rs13183555
[53]	凡志邈, 李海林, 夏伟杰, 等. 基于深度学习的成像声纳水下目标的检测与分类[C]//中国声学学会水声学分会2019年学术会议论文集. 南京, 中国: 中国声学学会水声学分会, 2019.
[54]	李宝奇, 黄海宁, 刘纪元, 等. 基于改进SSD的合成孔径声呐图像水下多尺度目标轻量化检测模型[J]. 电子与信息学报, 2021, 43(10): 2854-62. doi: 10.11999/JEIT201042 Li Baoqi, Huang Haining, Liu Jiyuan, et al. Synthetic aperture sonar underwater multi-scale target efficient detection model based on improved single shot detector[J]. Journal of Electronics & Information Technology, 2021, 43(10): 2854-62. doi: 10.11999/JEIT201042
[55]	Carion N, Massa F, Synnaeve G, et al. End-to-end object detection with transformers[C]//European Conference on Computer Vision. Cham: Springer, 2020: 213-229.
[56]	Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J]. Advances in Neural Information Processing Systems, 2017, 30: 5998-6008.
[57]	汤寓麟, 李厚朴, 张卫东, 等. 侧扫声纳检测沉船目标的轻量化DETR-YOLO法[J]. 系统工程与电子技术, 2022, 44(8): 2427-2436. doi: 10.12305/j.issn.1001-506X.2022.08.06 Tang Yulin, Li Houpu, Zhang Weidong, et al. Lightweight DETR-YOLO method for detecting shipwreck target in side-scan sonar[J]. Systems Engineering and Electronics, 2022, 44(8): 2427-2436. doi: 10.12305/j.issn.1001-506X.2022.08.06
[58]	Patel V M, Gopalan R, Li R, et al. Visual domain adaptation: A survey of recent advances[J]. Signal Processing Magazine, IEEE, 2015, 32(3): 53-69. doi: 10.1109/MSP.2014.2347059
[59]	朱兆彤, 付学志, 胡友峰. 一种利用迁移学习训练卷积神经网络的声呐图像识别方法[J]. 水下无人系统学报, 2020, 28(1): 89-96. doi: 10.11993/j.issn.2096-3920.2020.01.013 Zhu Zhaotong, Fu Xuezhi, Hu Youfeng. A sonar image recognition method based on convolutional neural network trained through transfer learning[J]. Journal of Unmanned Undersea Systems, 2020, 28(1): 89-96. doi: 10.11993/j.issn.2096-3920.2020.01.013
[60]	武铄, 王晓, 张丹阳, 等. 联合迁移学习和深度学习的侧扫声呐沉船识别方法[J]. 河南科技, 2021, 40(36): 36-40. doi: 10.3969/j.issn.1003-5168.2021.36.015
[61]	于淼. 基于深度学习的侧扫声呐图像目标检测方法研究[D]. 哈尔滨: 哈尔滨工程大学, 2020.
[62]	盛子旗, 霍冠英. 样本仿真结合迁移学习的声呐图像水雷检测[J]. 智能系统学报, 2021, 16(2): 385-392. doi: 10.11992/tis.202101030 Sheng Ziqi, Huo Guanying. Detection of underwater mine target in side scan sonar image based on sample simulation and transfer learning[J]. CAAI Transactions on Intelligent Systems, 2021, 16(2): 385-392. doi: 10.11992/tis.202101030
[63]	Tang Y, Jin S, Bian G, et al. Shipwreck target recognition in side-scan sonar images by improved YOLOv3 model based on transfer learning[J]. IEEE Access, 2020, 8: 173450-173460. doi: 10.1109/ACCESS.2020.3024813
[64]	付同强, 胡桥, 刘钰, 等. 基于优化二维变分模态分解与迁移学习的水下目标识别方法[J]. 水下无人系统学报, 2021, 29(2): 153-163. doi: 10.11993/j.issn.2096-3920.2021.02.004 Fu Tongqiang, Hu Qiao, Liu Yu, et al. Underwater target identification method based on optimized 2D variational mode decomposition and transfer learning[J]. Journal of Unmanned Undersea Systems, 2021, 29(2): 153-163. doi: 10.11993/j.issn.2096-3920.2021.02.004