Turn off MathJax
Article Contents
HE Mengyun, HE Zifen, ZHANG Yinhui, CHEN Guangchen, . Weak feature confocal channel regulation for underwater sonar target detection[J]. Chinese Optics. doi: 10.37188/CO.2024-0031
Citation: HE Mengyun, HE Zifen, ZHANG Yinhui, CHEN Guangchen, . Weak feature confocal channel regulation for underwater sonar target detection[J]. Chinese Optics. doi: 10.37188/CO.2024-0031

Weak feature confocal channel regulation for underwater sonar target detection

doi: 10.37188/CO.2024-0031
Funds:  Supported by the National Natural Science Foundation of China ( No. 62171206,No.62061022)
More Information
  • Corresponding author: zyhhzf1998@163.com
  • Received Date: 05 Feb 2024
  • Accepted Date: 26 Apr 2024
  • Available Online: 17 May 2024
  • Visual detection of sonar image is one of the important technologies in the field of resource exploration in complex waters and underwater foreign object target detection. Aiming at the problem of weak features and background information interference of small targets in sonar images, this paper proposes a weak feature confocal channel modulation algorithm for underwater sonar target detection. Firstly, in order to improve the model's ability to capture and characterize the information of weak targets, we design a weak target-specific activation strategy and introduce an a priori frame scale calibration mechanism to match the underlying semantic feature detection branch to improve the accuracy of small target detection; secondly, we apply the global information aggregation module to deeply excavate the global features of weak targets to avoid the redundant information from covering the small target's weak key features; lastly, in order to solve the problem of the traditional space pyramid Finally, in order to solve the problem of traditional spatial pyramid pooling which is easy to ignore the channel information, the confocal channel regulation pooling module is proposed to retain the effective channel domain small target information and overcome the interference of complex background information. Experiments show that the model in this paper achieves an average detection accuracy of 83.3% on nine types of weak targets in the underwater sonar dataset, which is 5.5% higher than the benchmark, among which the detection accuracy of iron bucket, human body model and cube is significantly improved by 24%, 8.6% and 7.3%, respectively, which effectively improves the problem of leakage and misdetection of weak targets in the underwater complex environment.

     

  • loading
  • [1]
    王芳. 新时期海洋强国建设形势与任务研究[J]. 中国海洋大学学报(社会科学版),2020(5):11-19.

    WANG F. Research on the situation and tasks of building a strong maritime power in the new era[J]. Journal of Ocean University of China (Social Sciences), 2020(5): 11-19. (in Chinese).
    [2]
    CLAY C S, HORNE J K. Acoustic models of fish: the Atlantic cod (Gadus morhua)[J]. The Journal of the Acoustical Society of America, 1994, 96(3): 1661-1668. doi: 10.1121/1.410245
    [3]
    HARLEY H E, DELONG C M. Echoic object recognition by the bottlenose dolphin[J]. Comparative Cognition & Behavior Reviews, 2008, 3: 46-65.
    [4]
    谭亦秋. 基于直流超导量子干涉仪的水下铁磁性目标探测技术研究[D]. 长沙: 国防科技大学, 2020.

    TAN Y Q. Research on detection technology of underwater ferromagnetic target based on DC superconducting quantum interference device[D]. Changsha: National University of Defense Technology, 2020. (in Chinese).
    [5]
    陈正想, 卢俊杰. 弱磁探测技术发展现状[J]. 水雷战与舰船防护,2011,19(4):1-5,24.

    CHEN ZH X, LU J J. Current development of weak magnetic detection[J]. Mine Warfare & Ship Self-Defence, 2011, 19(4): 1-5,24. (in Chinese).
    [6]
    XU W H, YANG J M, WEI H D, et al. A localization algorithm based on pose graph using Forward-looking sonar for deep-sea mining vehicle[J]. Ocean Engineering, 2023, 284: 114968. doi: 10.1016/j.oceaneng.2023.114968
    [7]
    罗逸豪. 基于深度学习的声呐图像目标检测系统[J]. 数字海洋与水下攻防,2023,6(4):423-428.

    LUO Y H. Sonar image object detection system based on deep learning[J]. Digital Ocean & Underwater Warfare, 2023, 6(4): 423-428. (in Chinese).
    [8]
    ISHAK A B. A two-dimensional multilevel thresholding method for image segmentation[J]. Applied Soft Computing, 2017, 52: 306-322. doi: 10.1016/j.asoc.2016.10.034
    [9]
    ZHANG B M, ZHOU T, SHI ZI F, et al. An underwater small target boundary segmentation method in forward-looking sonar images[J]. Applied Acoustics, 2023, 207: 109341. doi: 10.1016/j.apacoust.2023.109341
    [10]
    万广南. 基于激光和超声的水下目标探测方法研究[D]. 哈尔滨: 哈尔滨工业大学, 2017.

    WAN G N. Research on underwater target detection based laser and ultrasound[D]. Harbin: Harbin Institute of Technology, 2017. (in Chinese).
    [11]
    翟厚曦, 江泽林, 张鹏飞, 等. 一种合成孔径声呐图像目标分割方法[J]. 仪器仪表学报,2016,37(4):887-894. doi: 10.3969/j.issn.0254-3087.2016.04.022

    ZHAI H X, JIANG Z L, ZHANG P F, et al. Object segmentation method for synthetic aperture sonar images[J]. Chinese Journal of Scientific Instrument, 2016, 37(4): 887-894. (in Chinese). doi: 10.3969/j.issn.0254-3087.2016.04.022
    [12]
    杨卫东, 叶长彬, 陈正林, 等. 基于snake算法的声呐图像轮廓提取方法[J]. 压电与声光,2023,45(5):752-758.

    YANG W D, YE CH B, CHEN ZH L, et al. Image contour extraction method based on snake algorithm[J]. Piezoelectrics & Acoustooptics, 2023, 45(5): 752-758. (in Chinese).
    [13]
    胡钢. 基于深度学习的水下目标识别和运动行为分析技术研究[D]. 哈尔滨: 哈尔滨工程大学, 2021.

    HU G. Research on underwater target recognition and motion behavior analysis technology based on deep learning[D]. Harbin: Harbin Engineering University, 2021. (in Chinese).
    [14]
    DIVYABARATHI G, SHAILESH S, JUDY M V. Object classification in underwater SONAR images using transfer learning based ensemble model[C]. Proceedings of 2021 International Conference on Advances in Computing and Communications, IEEE, 2021: 1-4.
    [15]
    CHANDRASHEKAR G, RAAZA A, RAJENDRAN V, et al. Side scan sonar image augmentation for sediment classification using deep learning based transfer learning approach[J]. Materials Today: Proceedings, 2023, 80: 3263-3273. doi: 10.1016/j.matpr.2021.07.222
    [16]
    KONG W Z, HONG J CH, JIA M Y, et al. YOLOv3-DPFIN: a dual-path feature fusion neural network for robust real-time sonar target detection[J]. IEEE Sensors Journal, 2020, 20(7): 3745-3756. doi: 10.1109/JSEN.2019.2960796
    [17]
    REDMON J, FARHADI A. YOLOv3: an incremental improvement[J]. arXiv preprint arXiv: 1804.02767, 2018. (查阅网上资料, 请核对文献类型及格式) .
    [18]
    FAN X N, LU L, SHI P F, 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
    [19]
    BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[J]. arXiv preprint arXiv: 2004.10934, 2020. (查阅网上资料, 请核对文献类型及格式) .
    [20]
    GERG I D, MONGA V. Structural prior driven regularized deep learning for sonar image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 4200416.
    [21]
    NANDHINI S, ASHOKKUMAR K. An automatic plant leaf disease identification using DenseNet-121 architecture with a mutation-based henry gas solubility optimization algorithm[J]. Neural Computing and Applications, 2022, 34(7): 5513-5534. doi: 10.1007/s00521-021-06714-z
    [22]
    ZHU X Y, LIANG Y SH, ZHANG J L, et al. STAFNet: swin transformer based anchor-free network for detection of forward-looking sonar imagery[C]. Proceedings of the 2022 International Conference on Multimedia Retrieval, ACM, 2022: 443-450.
    [23]
    LIU Z, LIN Y T, CAO Y, et al. Swin transformer: hierarchical vision transformer using shifted windows[C]. Proceedings of 2021 IEEE/CVF International Conference on Computer Vision, IEEE, 2021: 10012-10022.
    [24]
    刘彦磊, 李孟喆, 王宣宣. 轻量型YOLOv5s车载红外图像目标检测[J]. 中国光学(中英文),2023,16(5):1045-1055. doi: 10.37188/CO.2022-0254

    LIU Y L, LI M ZH, WANG X X. Lightweight YOLOv5s vehicle infrared image target detection[J]. Chinese Optics, 2023, 16(5): 1045-1055. doi: 10.37188/CO.2022-0254
    [25]
    朱威, 王立凯, 靳作宝, 等. 引入注意力机制的轻量级小目标检测网络[J]. 光学 精密工程,2022,30(8):998-1010. doi: 10.37188/OPE.20223008.0998

    ZHU W, WANG L K, JIN Z B, et al. Lightweight small object detection network with attention mechanism[J]. Optics and Precision Engineering, 2022, 30(8): 998-1010. (in Chinese). doi: 10.37188/OPE.20223008.0998
    [26]
    乔美英, 史建柯, 李冰锋, 等. 改进损失函数的增强型FPN水下小目标检测[J]. 计算机辅助设计与图形学学报,2023,35(4):525-537.

    QIAO M Y, SHI J K, LI B F, et al. Enhanced FPN underwater small target detection with improved loss function[J]. Journal of Computer-Aided Design & Computer Graphics, 2023, 35(4): 525-537. (in Chinese).
    [27]
    张艳, 李星汕, 孙叶美, 等. 基于通道注意力与特征融合的水下目标检测算法[J]. 西北工业大学学报,2022,40(2):433-441. doi: 10.3969/j.issn.1000-2758.2022.02.025

    ZHANG Y, LI X SH, SUN Y M, et al. Underwater object detection algorithm based on channel attention and feature fusion[J]. Journal of Northwestern Polytechnical University, 2022, 40(2): 433-441. (in Chinese). doi: 10.3969/j.issn.1000-2758.2022.02.025
    [28]
    LI L, LI Y P, YUE CH H, et al. Real-time underwater target detection for AUV using side scan sonar images based on deep learning[J]. Applied Ocean Research, 2023, 138: 103630. doi: 10.1016/j.apor.2023.103630
    [29]
    WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]. Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2023: 7464-7475.
    [30]
    张绍文, 史卫亚, 张世强, 等. 基于加权感受野和跨层融合的遥感小目标检测[J]. 电子测量技术,2023,46(18):129-138.

    ZHANG SH W, SHI W Y, ZHANG SH Q, et al. Remote sensing small target detection based on weighted receptive field and cross-layer fusion[J]. Electronic Measurement Technology, 2023, 46(18): 129-138. (in Chinese).
    [31]
    CUI L SH, LV P, JIANG X H, et al. Context-aware block net for small object detection[J]. IEEE Transactions on Cybernetics, 2022, 52(4): 2300-2313. doi: 10.1109/TCYB.2020.3004636
    [32]
    YU F, KOLTUN V. Multi-scale context aggregation by dilated convolutions[J]. arXiv preprint arXiv: 1511.07122, 2015. (查阅网上资料, 请核对文献类型及格式) .
    [33]
    WANG C Y, LIAO H Y M, WU Y H, et al. CSPNet: a new backbone that can enhance learning capability of CNN[C]. Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, IEEE, 2020: 390-391.
    [34]
    https://openi.pcl.ac.cn/OpenOrcinus_orca. .
    [35]
    刘颖, 刘红燕, 范九伦, 等. 基于深度学习的小目标检测研究与应用综述[J]. 电子学报,2020,48(3):590-601. doi: 10.3969/j.issn.0372-2112.2020.03.024

    LIU Y, LIU H Y, FAN J L, et al. A survey of research and application of small object detection based on deep learning[J]. Acta Electronica Sinica, 2020, 48(3): 590-601. (in Chinese). doi: 10.3969/j.issn.0372-2112.2020.03.024
    [36]
    CHEN CH Y, LIU M Y, TUZEL O, et al. R-CNN for small object detection[C]. Proceedings of the 13th Asian Conference on Computer Vision, Springer, 2017: 214-230.
    [37]
    高新波, 莫梦竟成, 汪海涛, 等. 小目标检测研究进展[J]. 数据采集与处理,2021,36(3):391-417.

    GAO X B, MO M J CH, WANG H T, et al. Recent advances in small object detection[J]. Journal of Data Acquisition and Processing, 2021, 36(3): 391-417. (in Chinese).
    [38]
    LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[C]. Proceedings of the 14th European Conference, Springer, 2016: 21-37.
    [39]
    WANG Y Y, WANG CH, ZHANG H, et al. Automatic ship detection based on RetinaNet using multi-resolution Gaofen-3 imagery[J]. Remote Sensing, 2019, 11(5): 531. doi: 10.3390/rs11050531
    [40]
    JOCHER G, STOKEN A, BOROVEC J, et al. Ultralytics/yolov5: v3.0[Z]. Zenodo, 2020. (查阅网上资料, 请核对文献类型及格式) .
    [41]
    REN SH Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149. doi: 10.1109/TPAMI.2016.2577031
    [42]
    XU X ZH, JIANG Y Q, CHEN W H, et al. DAMO-YOLO: a report on real-time object detection design[J]. arXiv preprint arXiv: 2211.15444, 2022. (查阅网上资料, 请核对文献类型及格式) .
    [43]
    HUSSAIN M. YOLO-v1 to YOLO-v8, the rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection[J]. Machines, 2023, 11(7): 677. doi: 10.3390/machines11070677
    [44]
    WU Q, ZHANG B, XU CH G, et al. Dense oil tank detection and classification via YOLOX-TR network in large-scale SAR images[J]. Remote Sensing, 2022, 14(14): 3246. doi: 10.3390/rs14143246
    [45]
    MA L, ZHAO L Y, WANG Z X, et al. Detection and counting of small target apples under complicated environments by using improved YOLOv7-tiny[J]. Agronomy, 2023, 13(5): 1419. doi: 10.3390/agronomy13051419
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(5)  / Tables(8)

    Article views(51) PDF downloads(5) Cited by()
    Proportional views

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return