任务驱动下成像声呐水下目标识别方法综述

doi:10.13229/j.cnki.jdxbgxb.20240002

摘要/Abstract

摘要：

阐述了侧扫声呐、合成孔径声呐和前视声呐在图像分类、检测以及分割任务中的目标识别算法及其解决的主要问题。通过结合不同声呐的成像特点与应用场景，分析总结上述成像声呐对应图像处理任务下的目标识别算法的优劣及仍需解决的关键问题，并展望其未来发展方向。

关键词: 信息处理技术, 水下目标识别, 成像声呐, 图像处理

Abstract:

The target recognition algorithms and the major problems solved in image classification， detection， and segmentation tasks for side-scan sonar， synthetic aperture sonar， and forward-looking sonar were described. By combining the imaging characteristics and application scenarios of different sonars， the strengths and weaknesses of the target recognition algorithms under the corresponding image processing tasks of the above imaging sonar and the key issues that still need to be addressed were analyzed and summarized， and its future development direction was also looked forward to.

Key words: information processing technology, underwater target recognition, imaging sonar, image processing

中图分类号:

TN911.73

聂为之,尹斐,苏毅珊. 任务驱动下成像声呐水下目标识别方法综述[J]. 吉林大学学报(工学版), 2025, 55(4): 1163-1175.

Wei-zhi NIE,Fei YIN,Yi-shan SU. Review of task⁃driven imaging sonar for underwater target recognition approaches[J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(4): 1163-1175.

图/表 14

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

图11

图12

图13

图14

参考文献 79

1	Kim H G, Seo J, Kim S M. Underwater optical-sonar image fusion system[J]. Sensors, 2022, 22(21): 8445-8457.
2	Kumar N, Mitra U, Narayanan S S. Robust object classification in underwater sidescan sonar images by using reliability-aware fusion of shadow features[J]. IEEE Journal of Oceanic Engineering, 2015, 40(3): 592-606.
3	罗逸豪, 刘奇, 张吟, 等. 基于深度学习的水下图像目标检测综述[J]. 电子与信息学报, 2023, 45(6): 1-15.
	Luo Yi-hao, Liu Qi, Zhang Yin, et al. Review of underwater image object detection based on deep learning[J]. Journal of Electronics & Information Techno-logy, 2023, 45(6): 1-15.
4	Chen H Y, Zhang H Q. Combined fuzzy clustering and Chan Vese model for multicolor gradual ochotona curzoniae image segmentation[J]. Mathematical Problems in Engineering, 2021, 2021(1): 1-9.
5	Ge Q, Ruan F, Qiao B, et al. Side-scan sonar image classification based on style transfer and pre-trained convolutional neural networks[J]. Electronics, 2021, 10(15): 1823-1838.
6	Oliveira A J, Ferreira B M, Diamant R, et al. Sonar-based cable detection for in-situ calibration of marine sensors[C]∥IEEE OES Autonomous Underwater Vehicles, Singapore, 2022:1-6.
7	Chen R Y, Chen Y. Improved convolutional neural network YOLOv5 for underwater target detection based on autonomous underwater helicopter[J]. Journal of Marine Science and Engineering, 2023, 11(5): 989-1002.
8	Mignotte M. Saliency map estimation using a pixel-pairwise-based unsupervised markov random field model[J]. Mathematics, 2023, 11(4): 986-997.
9	Zhang B M, Zhong T, Shi Z F, et al. An underwater small target boundary segmentation method in forward- looking sonar images[J]. Applied Acoustics, 2023, 207(2): 109341-109350.
10	Yang Z W, Zhao J H, Zhang H M, et al. A side-scan sonar image synthesis method based on a diffusion model[J]. Journal of Marine Science and Engineering, 2023, 11(6): 1103-1115.
11	Dura E, Bell J, Lane D. Superellipse fitting for the recovery and classification of mine-like shapes in sidescan sonar images[J]. IEEE Journal of Oceanic Engineering, 2008, 33(4): 434-444.
12	Reed S, Petilot Y, Bell J. A model based approach to mine detection and classification in sidescan sonar[C]∥Oceans, San Diego, USA, 2003: 1402-1407.
13	Sun J, Fan M, Cui X D, et al. A multibeam seafloor classification method combining ReliefF and random forest model[J]. Marine Science Bulletin, 2022, 41: 131-139.
14	Rhinelander J. Feature extraction and target classification of side-scan sonar images[C]∥IEEE Symposium Series on Computational Intelligence (SSCI), Athens, Greece, 2016: 1-6.
15	熊明宽, 吴自银, 李守军, 等. 基于SVM的海底声纳图像底质识别[J]. 海洋通报, 2012, 2012(4): 409-414.
	Xiong Ming-kuan, Wu Zi-yin, Li Shou-jun, et al. Seafloor sonar sediment image recognition with the SVM[J]. Marine Science Bulletin, 2012, 2012(4): 409-414.
16	郭军, 马金凤, 王爱学. 基于SVM算法和GLCM的侧扫声纳影像分类研究[J]. 测绘与空间地理信息, 2015, 38(3): 4-13.
	Guo Jun, Ma Jin-feng, Wang Ai-xue. Study of side scan sonar image classification based on SVM and GLCM[J]. Geomatics & Spatial Information Technology, 2015, 38(3): 4-13.
17	Wang J C, Wu Z Y, Wang M W, et al. ELM-AdaBoost method of acoustic seabed sediment classification[J]. Acta Oceanologica Sinica, 2021, 43: 144-151.
18	Yang F L, Liu J N. Seabed classification using BP Neural Network based on GA[J]. Acta Oceanologica Sinica, 2006, 4: 523-531.
19	熊明宽, 吴自银, 李守军, 等. 基于遗传小波神经网络的海底声学底质识别分类[J]. 海洋学报, 2014, 36(5): 90-97.
	Xiong Ming-kuan, Wu Zi-yin, Li Shou-jun, et al. Wavelet neural network identification and classification of sediment seabed sonar images based on genetic algorithms[J]. Acta Oceanologica Sinica, 2014, 36(5): 90-97.
20	Luo X, Qin X, Wu Z, et al. Sediment classification of small-size seabed acoustic images using convolutional neural networks[J]. IEEE Access, 2019, 7: 98331-98339.
21	Huo G, Wu Z, Li J. Underwater object classification in sidescan sonar images using deep transfer learning and semisynthetic training data[J]. IEEE Access, 2020, 8: 47407-47418.
22	Petillot Y R, Reed S R, Bell J M. Real time AUV pipeline detection and tracking using side scan sonar and multi-beam echo-sounder[C]∥Oceans, Biloxi, USA, 2002:217-222.
23	Fakiris E, Papatheodorou G, Geraga M, et al. An automatic target detection algorithm for swath sonar backscatter imagery, using image texture and independent component analysis[J]. Remote Sensing, 2016, 8(5): 373-380.
24	Zheng L Y, Tian K. Detection of small objects in sidescan sonar images based on POHMT and Tsallis entropy[J].Signal Processing the Official Publication of the European Association for Signal Processing, 2018, 142: 168-177.
25	Sawas J, Petillot Y, Pailhas Y. Cascade of boosted classifiers for rapid detection of underwater objects[C]∥The 10th European Conference on Underwater Acoustics, Istanbul, Turkey, 2010:1507-1516.
26	Tang Y L, Jin S H, 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.
27	Ma Q, Jiang L, Yu W, et al. Training with noise adversarial network: a generalization method for object detection on sonar image[C]∥Workshop on Applications of Computer Vision, Snowmass, USA, 2020:188-196.
28	Jiang L, Cai T, Ma Q, et al. Active object detection in sonar images[J]. IEEE Access, 2020, 8: 102540-102553.
29	Topple J M, Fawcett J A. MiNet: efficient deep learning automatic target recognition for small autonomous vehicles[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(6): 1014-1018.
30	Wang Z, Zhang S, Huang W, et al. Sonar image target detection based on adaptive global feature enhancement network[J]. IEEE Sensors Journal, 2022, 22(2): 1509-1530.
31	Tang X Y, Zhang X W, Xu X L, et al. Methods for underwater sonar image processing in objection detection[C]∥International Conference on Computer Systems, Electronics and Control(ICCSEC), Dalian, China, 2017:941-944.
32	Celik T, Tjahjadi T. A novel method for sidescan sonar image segmentation[J]. IEEE Journal of Oceanic Engineering, 2011, 36(2): 186-194.
33	Mignotte M, Collet C, Pérez P, et al. Sonar image segmentation using an unsupervised hierarchical MRF model[J]. IEEE Transactions on Image Processing, 2000, 9(7): 1216-1231.
34	Ye X F, Zhang R H, Liu R X, et al. Sonar image segmentation based on GMRF and level-set models[J]. Ocean Engineering, 2010, 37(10): 891-901.
35	Huo G, Yang S X, Li Q, et al. A robust and fast method for sidescan sonar image segmentation using nonlocal despeckling and active contour model[J]. IEEE Transactions on Cybernetics, 2016, 47(4): 1-18.
36	Wu M, Wang Q, Rigall E, et al. ECNet: efficient convolutional networks for side scan sonar image segmentation[J]. Sensors, 2019, 19(9): 2009-2010.
37	Wei B, He C L, Xing S Y, et al. Accelerated deconvolved imaging algorithm for 2D multibeam synthetic aperture sonar[J]. Sensors, 2022, 22(16): 6016-6025.
38	Hollesen P, Connors W A, Trappenberg T P. Comparison of learned versus engineered features for classification of mine like objects from raw sonar images[C]∥Canadian Conference on Advances in Artificial Intelligence, Hollensen, Canada, 2011: 174-185.
39	Mckay J, Monga V, Raj R G. Robust sonar ATR through bayesian pose corrected sparse classification[J]. IEEE Transactions on Geoscience & Remote Sensing, 2017, 55(10): 5563-5576.
40	Fei T, Kraus D, Zoubir A M. Contributions to automatic target recognition systems for underwater mine classification[J]. IEEE Transactions on Geoscience & Remote Sensing, 2014, 53(1): 505-518.
41	Williams D P, Fakiris E. Exploiting environmental information for improved underwater target classification in sonar imagery[J]. IEEE Transactions on Geoscience & Remote Sensing, 2014, 52(10): 6284-6297.
42	Denos K, Ravaut M, Fagette A, et al. Deep learning applied to underwater mine warfare[C]∥Oceans, Aberdeen, UK, 2017:1-7.
43	Warakagoda N, Midtgaard I. Transfer-learning with deep neural networks for mine recognition in sonar images[C]∥Synthetic Aperture Sonar, Synthetic Aperture Radar, Lerici, Italy, 2018:1-5.
44	Williams D P. Transfer learning with SAS-image convolutional neural networks for improved underwater target classification[C]∥IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019: 78-81.
45	Klausner N, Azimi-Sadjadi M R. Non-Gaussian target detection in sonar imagery using the multivariate laplace distribution[J]. IEEE Journal of Oceanic Engineering, 2015, 40(2): 452-464.
46	Abu A, Diamant R. A statistically-based method for the detection of underwater objects in sonar imagery[J]. Sensors Journal, 2019, 19(16): 6858-6871.
47	Azimi-Sadjadi M R, Klausner N, Kopacz J. Detection of underwater targets using a subspace-based method with learning[J]. IEEE Journal of Oceanic Engineering, 2017, 42(4): 869-879.
48	Sawas J, Petillot Y. Cascade of boosted classifiers for automatic target recognition in synthetic aperture sonar imagery[C]∥Meetings on Acoustics, Edinburgh, Scotland, 2012: 1-11.
49	Williams D P. Fast target detection in synthetic aperture sonar imagery: a new algorithm and large-scale performance analysis[J]. IEEE Journal of Oceanic Engineering, 2015, 40(1): 71-92.
50	Berthomier T, Williams D P, Dugelay S. Target localization in synthetic aperture sonar imagery using convolutional neural networks[C]∥OCEANS MTS/IEEE SEATTLE, Seattle, USA, 2019: 1-9.
51	Maussang F, Chanussot J, Hetet A, et al. Mean–standard deviation representation of sonar images for echo detection: application to SAS images[J]. IEEE Journal of Oceanic Engineering, 2008, 32(4): 956-970.
52	Peeples J, Cook M, Suen D, et al. Comparison of possibilistic fuzzy local information c-means and possibilistic k-nearest neighbors for synthetic aperture sonar image segmentation[J]. Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, 2019, 19(4): 1904-1914.
53	Abu A, Diamant R. Enhanced fuzzy-based local information algorithm for sonar image segmentation[J]. IEEE Transactions on Image Processing, 2019, 29: 445-460.
54	Bell J M, Petillot Y R, Lebart K, et al. Target recognition in synthetic aperture and high resolution sidescan sonar[C]∥IET Seminar on High Resolution Imaging and Target Classification, London, UK, 2006: 99-106.
55	Lu Y C, Sang E F. Underwater target's size/shape dynamic analysis for fast target recognition using sonar images[C]∥Proceedings of International Symposium on Underwater Technology, Tokyo, Japan, 1998:257-262.
56	Dos Santos M, Ribeiro P O, Núñez P, et al. Object classification in semi structured enviroment using forward-looking Sonar[J]. Sensors, 2017, 17(10): 2235-2251.
57	Jin L, Liang H, Ang C. Accurate underwater ATR in forward-looking sonar imagery using deep convolutional neural networks[J]. IEEE Access, 2019, 7: 125522-125531.
58	Valdenegro-Toro M. Object recognition in forward-looking sonar images with convolutional neural networks[C]∥OCEANS MTS/IEEE Monterey, Monterey, USA, 2016: 1-6.
59	Nguyen H T, Lee E H, Lee S. Study on the classification performance of underwater sonar image classification based on convolutional neural networks for detecting a submerged human body[J]. Sensors(Basel, Switzerland), 2020, 20(1): 94-100.
60	Valdenegro-Toro M. Deep neural networks for marine debris detection in sonar images[D]. Edinburgh: Engineering & Physical Sciences, Heriot-Watt University, 2019.
61	Wang W W, Cheng B B, Chen Y. A real-time object recognition for forward looking sonar[C]∥The 2nd International Conference on Image, Vision and Computing (ICIVC), Chengdu, China, 2017: 58-61.
62	Hurtós N, Palomeras N, Nagappa S, et al. Automatic detection of underwater chain links using a forward-looking sonar[C]∥MTS/IEEE OCEANS, Bergen, Norway, 2013: 1-7.
63	Zacchini L, Ridolfi A, Topini A, et al. Deep learning for on-board AUV automatic target recognition for optical and acoustic imagery[J]. IFAC, 2020, 53(2): 14589-14594.
64	Wu Y C. Sonar image target detection and recognition based on convolution neural network[J]. Mobile Information Systems, 2021, 2021(6): 1-8.
65	Kvasić I, Mišković N, Vukić Z. Convolutional neural network architectures for sonar-based diver detection and tracking[C]∥Oceans,Marseille, France, 2019: 1-6.
66	Zacchini L, Topini A, Franchi M, et al. Autonomous underwater environment perceiving and modeling: an experimental campaign with FeelHippo AUV for forward looking sonar-based automatic target recognition and data association[J]. IEEE Journal of Oceanic Engineering, 2022, 48: 277-296.
67	Lou G, Zheng R, Liu M, et al. Automatic target recognition in forward-looking sonar images using transfer learning[C]∥Global Oceans, Biloxi, USA, 2020: 1-6.
68	Zhang P, Tang J, Zhong H, et al. Self-trained target detection of radar and sonar images using automatic deep learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-14.
69	Qin R, Zhao X, Zhu W, et al. Multiple receptive field network (MRF-Net) for autonomous underwater vehicle fishing net detection using forward-looking sonar images[J]. Sensors, 2021, 21(6): 1933-1945.
70	Song S, Si B, Feng X, et al. Label field initialization for MRF-based sonar image segmentation by selective autoencoding[C]∥Oceans, Shanghai, China, 2016: 1-5.
71	Liang Y, Zhu X, Zhang J. Maanu-Net: multi-level attention and atrous pyramid nested U-Net for wrecked objects segmentation in forward-looking sonar images[C]∥IEEE International Conference on Image Processing(ICIP), Bordeaux, France, 2022: 736-740.
72	Liang Y, Zhu X, Zhang J. MiTU-Net: an efficient mix transformer U-like network for forward-looking sonar image segmentation[C]∥IEEE 2nd International Conference on Computer Communication and Artificial Intelligence(CCAI), Beijing, China, 2022: 149-154.
73	Jegorova M, Karjalainen A I, Vazquez J, et al. R2D2-GAN: unlimited resolution image generation for acoustic data[J]. Marine Technology Society Journal, 2021, 55(4): 99-107.
74	Chungath T T, Nambiar A M, Mittal A. Transfer learning and few-shot learning based deep neural network models for underwater sonar image classification with a few samples[J]. IEEE Journal of Oceanic Engineering, 2022, 49(1): 294-310.
75	Muthuraman D L, Santhanam S M. Contrast improvement on side scan sonar images using retinex based edge preserved technique[J]. Mar Geophys Res, 2022, 43(17): 1-19.
76	Jiao W, Zhang J. Sonar images classification while facing long-tail and few-shot[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-20.
77	薛雅丽, 俞潼安, 崔闪, 等. 基于级联嵌套U-Net的红外小目标检测[J/OL].[2024-01-13]. 吉林大学学报:工学版, .
	Xue Ya-li, Yu Tong-an, Cui Shan, et al. Infrared small target detection based on cascaded nested U-Net[J/OL].[2025-01-13]. Journal of Jilin University (Engineering and Technology Edition),.
78	刘萍萍, 商文理, 解小宇, 等. 基于细粒度分析的不均衡图像分类算法[J/OL].[2025-01-13]. 吉林大学学报:工学版, .
	Liu Ping-ping, Shang Wen-li, Xie Xiao-yu, et al. An unbalanced image classification algorithm based on fine-grained analysis[J/OL]. [2025-01-13].Journal of Jilin University(Engineering and Technology Edition),
79	Wu T, Tang S, Zhang R, et al. CGNet: a light-weight context guided network for semantic segmentation[J]. IEEE Transactions on Image Processing, 2021, 30: 1169-1179

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

[1]	张汝波,常世淇,张天一. 基于深度学习的图像信息隐藏方法综述[J]. 吉林大学学报(工学版), 2025, 55(5): 1497-1515.
[2]	薛雅丽,俞潼安,崔闪,周李尊. 基于级联嵌套U-Net的红外小目标检测[J]. 吉林大学学报(工学版), 2025, 55(5): 1714-1721.
[3]	单泽彪,薛泓垚,刘小松,姚瑞广,陈广秋. Alpha稳定分布噪声下基于近似l₀范数稀疏重构的波达方向估计[J]. 吉林大学学报(工学版), 2025, 55(3): 1093-1102.
[4]	方钰,梅德清,郑会龙,杨肖芳,武海龙,张晓武. 中国空间站燃烧科学柜火焰传播速度地面测量试验[J]. 吉林大学学报(工学版), 2024, 54(9): 2460-2468.
[5]	王德兴,高凯,袁红春,杨钰锐,王越,孔令栋. 基于色彩校正和TransFormer细节锐化的水下图像增强[J]. 吉林大学学报(工学版), 2024, 54(3): 785-796.
[6]	段锦,姚安妮,王震,于林韬. 改进的麻雀搜索算法优化无线传感器网络覆盖[J]. 吉林大学学报(工学版), 2024, 54(3): 761-770.
[7]	杨国俊,齐亚辉,石秀名. 基于数字图像技术的桥梁裂缝检测综述[J]. 吉林大学学报(工学版), 2024, 54(2): 313-332.
[8]	肖剑,刘经纬,胡欣,齐小刚. 基于改进非洲秃鹫算法的TDOA-AOA定位[J]. 吉林大学学报(工学版), 2024, 54(12): 3558-3567.
[9]	王勇,边宇霄,李新潮,徐椿明,彭刚,王继奎. 基于多尺度编码-解码神经网络的图像去雾算法[J]. 吉林大学学报(工学版), 2024, 54(12): 3626-3636.
[10]	肖明尧,李雄飞,朱芮. 基于NSST域像素相关分析的医学图像融合[J]. 吉林大学学报(工学版), 2023, 53(9): 2640-2648.
[11]	陈绵书,于录录,李晓妮,郑宏宇. 基于均匀ORB特征的回环检测算法[J]. 吉林大学学报(工学版), 2023, 53(9): 2666-2675.
[12]	苏育挺,王骥,赵玮,井佩光. 基于动态图卷积的图像情感分布预测[J]. 吉林大学学报(工学版), 2023, 53(9): 2601-2610.
[13]	金小俊,孙艳霞,于佳琳,陈勇. 基于深度学习与图像处理的蔬菜苗期杂草识别方法[J]. 吉林大学学报(工学版), 2023, 53(8): 2421-2429.
[14]	张振海,季坤,党建武. 基于桥梁裂缝识别模型的桥梁裂缝病害识别方法[J]. 吉林大学学报(工学版), 2023, 53(5): 1418-1426.
[15]	国强,朱国会,李万臣. 基于混沌麻雀搜索算法的TDOA/FDOA定位[J]. 吉林大学学报(工学版), 2023, 53(2): 593-600.