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弱特征共焦通道调控水下声呐目标检测

何梦云 何自芬 张印辉 陈光晨 张枫

何梦云, 何自芬, 张印辉, 陈光晨, 张枫. 弱特征共焦通道调控水下声呐目标检测[J]. 中国光学(中英文). doi: 10.37188/CO.2024-0031
引用本文: 何梦云, 何自芬, 张印辉, 陈光晨, 张枫. 弱特征共焦通道调控水下声呐目标检测[J]. 中国光学(中英文). doi: 10.37188/CO.2024-0031
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

弱特征共焦通道调控水下声呐目标检测

doi: 10.37188/CO.2024-0031
基金项目: 国家自然科学基金资助项目(No. 62171206,No. 62061022)
详细信息
    作者简介:

    何梦云(2000—),女,云南昆明人,硕士研究生,2022年于天津科技大学机械工程学院获得学士学位,现为昆明理工大学机电工程学院硕士研究生,主要从事图像处理、机器视觉及机器智能方面的研究。E-mail:18088400647@163.com

    何自芬(1976—),女,山西阳泉人,博士,教授,硕士生导师,2000年、2005年于西安理工大学分别获得学士和硕士学位,2013年于昆明理工大学获得博士学位,主要从事图像处理和机器视觉等方面的研究。E-mail:zyhhzf1998@163.com

    张印辉(1977—),男,河北故城人,博士,教授、博士生导师,2000年和2005年分别于西安理工大学获得学士学位和硕士学位,2010年于昆明理工大学获得博士学位,主要从事图像处理、机器视觉及机器智能等方面的研究。E-mail:zhangyinhui@kust.edu.cn

    陈光晨(1997—),男,安徽六安人,博士研究生,2020年于安徽工程大学获得学士学位,2023年于昆明理工大学获得硕士学位,现为昆明理工大学机电工程学院博士研究生。主要从事图像处理、机器视觉、深度学习等方面的研究。E-mail:guangchen_c@yeah.net

    张 枫(1998—),男,江苏南通人,硕士研究生,2021年于淮阴工学院机械与材料工程学院获得学士学位,现为昆明理工大学机电工程学院硕士研究生,主要从事图像处理、机器视觉及深度学习等方面的研究。E-mail:zf1977497475@163.com

  • 中图分类号: TP394.1;TH691.9

Weak feature confocal channel regulation for underwater sonar target detection

Funds: Supported by the National Natural Science Foundation of China ( No. 62171206,No.62061022)
More Information
  • 摘要:

    声呐图像视觉检测是复杂水域资源勘探和水下异物目标探测领域的重要技术之一。针对声呐图像中小目标存在特征微弱和背景信息干扰问题,本文提出弱特征共焦通道调控水下声呐目标检测算法。首先,为提高模型对弱小目标信息捕获和表征能力,设计弱小目标特征激活策略并引入先验框尺度校准机制匹配底层语义特征检测分支以提高小目标检测精度;其次,应用全局信息聚合模块深入挖掘弱小目标全局特征,避免冗余信息覆盖小目标微弱关键特征;最后,为解决传统空间金字塔池化易忽视通道信息的问题,提出共焦通道调控池化模块,保留有效通道域小目标信息并克服复杂背景信息干扰。实验表明,本文模型在水下声呐数据集的九类弱小目标识别上平均检测精度达到83.3%,相较基准提高5.5%,其中铁桶、人体模型和立方体检测精度得到显著提高,分别提高24%、8.6%和7.3%,有效改善水下复杂环境中弱小目标漏检和误检问题。

     

  • 图 1  WFCCMNet模型结构图

    Figure 1.  WFCCMNet model structure

    图 2  全局信息聚合模块结构图

    Figure 2.  Structure of global information aggregation module

    图 3  共焦通道调控池化模块结构图

    Figure 3.  Structure of confocal channel modulation pooling module

    图 4  数据实例分布图

    Figure 4.  Distribution of data instances and a priori frames

    图 5  检测结果可视化

    Figure 5.  Visualisation of detection results

    表  1  改进前后输出分支与先验框对应关系

    Table  1.   Correspondence between output branches and a priori boxes before and after improvement

    改进前 改进后
    输出
    分支
    先验框
    尺度
    输出
    分支
    先验框
    尺度
    - - P4 [(116,90),(156,198),(373,326)]
    N3 [(10,13),(16,30),(33,23)] P3 [(5,6),(8,14),(15,11)]
    N4 [(30,61),(62,45),(59,119)] P2 [(10,13),(16,30),(33,23)]
    N5 [(116,90),(156,198),(373,326)] P1 [(30,61),(62,45),(59,119)]
    下载: 导出CSV

    表  2  目标图像面积占比

    Table  2.   Target image area percentage

    Classes nameMaximum area of target frame(height×width)Ration
    ball156×1420.04
    circle cage128×1480.04
    cube140×1640.04
    cylinder122×1020.02
    human body215×2160.09
    metal bucket168×2060.07
    plane187×2780.10
    square cage130×1180.03
    tyre166×1660.05
    下载: 导出CSV

    表  3  改进模块消融实验

    Table  3.   Improved module ablation experiments

    GFLOPS mAP50 human body ball circle cage square cage tyre metal bucket cube cylinder plane
    Baseline 15.8 77.8 81.2 84.9 81.6 78.7 64.8 61.1 84.4 71.8 92.5
    +特征激活 18.6 80.1 87.6 82.5 81.1 80.9 64.9 68.8 86.4 77.5 91
    +GIAM 19.5 80.9 86.8 83.8 84.1 78.5 64.3 80.1 85.8 74.8 90.3
    +CFCRP 23.7 83.3 89.8 85.7 79.1 82.9 73.1 85.1 91.7 71.2 91
    下载: 导出CSV

    表  4  实验代号与输出分支和先验框尺度对应关系

    Table  4.   Correspondence of experiment codes with output layers and a priori boxes

    实验代号输出分支先验框尺度输出分支先验框尺度
    G1--N4[(30,61),(62,45),(59,119)]
    N3[(10,13),(16,30),(33,23)]N5[(116,90),(156,198),(373,326)]
    G2P4[(5,6),(8,14),(15,11)]P2[(30,61),(62,45),(59,119)]
    P3[(10,13),(16,30),(33,23)]P1[(116,90),(156,198),(373,326)]
    G3P4[(10,13),(16,30),(33,23)]P2[(30,61),(62,45),(59,119)]
    P3[(5,6),(8,14),(15,11)]P1[(116,90),(156,198),(373,326)]
    G4P4[(30,61),(62,45),(59,119)]P2[(10,13),(16,30),(33,23)]
    P3[(5,6),(8,14),(15,11)]P1[(116,90),(156,198),(373,326)]
    G5P4[(116,90),(156,198),(373,326)]P2[(10,13),(16,30),(33,23)]
    P3[(5,6),(8,14),(15,11)]P1[(30,61),(62,45),(59,119)]
    下载: 导出CSV

    表  5  检测分支与先验框组合定量实验结果

    Table  5.   Results of quantitative experiments on the combination of detection branch and a priori frame

    实验代号 GFLOPS mAP50 human body ball circle cage square cage tyre metal bucket cube cylinder plane
    G1 15.8 77.8 81.2 84.9 81.6 78.7 64.8 61.1 84.4 71.8 92.5
    G2 15.9 78.6 88.8 83.7 79.7 82.6 60.6 66.9 87.1 68.5 89.7
    G3 15.9 77.5 77.9 82.3 80.7 72.4 63.9 67.6 86.9 73.7 92.1
    G4 15.9 79.2 79.9 81.7 78.5 75.2 64.4 74.2 87.3 76.6 95.1
    G5 15.9 79.3 89.4 83.2 78.6 83.3 63 71.1 84.3 71.2 90
    下载: 导出CSV

    表  6  多尺度共焦卷积对比实验

    Table  6.   Multi-scale confocal convolution comparison experiments

    Base Model 共焦卷积核 GFLOPS mAP50 human body ball circle cage square cage tyre metal bucket cube cylinder plane
    YOLOv5s
    +特征激活
    +GIAM
    - 19.5 80.9 86.8 83.8 84.1 78.5 64.3 80.1 85.8 74.8 90.3
    +1×1 19.3 80.8 85.3 86.1 67.3 85.1 71.3 84.5 89.7 68.8 89.5
    +1×1
    +3×3
    19.4 81.4 89.4 84.1 78.8 83.3 66.1 87.5 87.1 68.7 87.5
    +1×1
    +3×3
    +5×5
    20.8 80.6 91.8 83.4 75 79.1 67.2 72.3 87.2 80.5 88.8
    +1×1
    +3×3
    +5×5
    +7×7
    23.7 83.3 89.8 85.7 79.1 82.9 73.1 85.1 91.7 71.2 91
    +1×1
    +3×3
    +5×5
    +7×7
    +9×9
    27.6 78.2 85.3 84.9 74 80.2 66.4 78.2 88.6 61.6 84.6
    下载: 导出CSV

    表  7  空间特征金字塔池化对比实验

    Table  7.   Spatial feature pyramid pooling comparison experiments

    Base Model 空间特征金字塔池化 GFLOPS mAP50 human body ball circle cage square cage tyre metal bucket cube cylinder plane
    YOLOv5s
    +特征激活
    +GIAM
    +SPPF 19.5 80.9 86.8 83.8 84.1 78.5 64.3 80.1 85.8 74.8 90.3
    +SPP 18.5 81.4 86.1 83.6 81.9 86.8 67.5 73.2 86.4 76.8 90.2
    +SPPFCSPC 23.6 73.9 78.1 79.7 71.6 76.5 60.3 60.4 85.4 62.1 91.2
    +ASPP 25.1 77 78.4 80.1 72.8 77.6 70 76.6 88 61.8 87.9
    +RFB 19.0 80.5 86.8 85.8 78.7 80.4 69.7 80.3 87.2 72.2 83.8
    +SPPCSPC 23.6 75.2 80.3 78.6 73.4 72.8 62.4 71.4 82.4 72.3 83.1
    +CFCRP 23.7 83.3 89.8 85.7 79.1 82.9 73.1 85.1 91.7 71.2 91
    下载: 导出CSV

    表  8  网络模型对比实验

    Table  8.   Network model comparison experiment

    Model GFLOPS mAP50 human body ball circle cage square cage tyre metal bucket cube cylinder plane
    SSD[38] 347.1 78.8 89.1 90.9 74.8 76.4 62.3 77.4 81 68.4 89
    RetinaNet[39] 207.9 68.4 89.8 77.5 59.8 74.7 47.4 75.9 78.6 24 87.9
    YOLOv5x[40] 203.9 77.4 76.1 83.8 76.8 72.7 62.3 80 83.8 66.2 95
    Faster RCNN[41] 193.8 71.2 80.4 81.1 77.6 75.7 45 70 81.8 40.4 88.7
    YOLOv7[29] 103.3 60.4 71.5 74.2 57.8 67 48.1 59.8 72.8 35.8 56.4
    YOLOv5m[40] 48 71.3 68.6 81.8 79 61.5 56.7 58.9 85.1 69.3 80.7
    DAMO-YOLO[42] 36 72.7 65.5 81.3 70.2 70.8 38.7 87.8 85.6 71.8 82.2
    YOLOv8s[43] 28.2 81.4 82.3 86.5 81.1 86.6 65 93.7 88.5 53.5 95
    YOLOXs[44] 26.7 82.4 88.1 88 75.9 87.2 71.7 79.3 89.6 71.4 90
    YOLOv5s[40] 15.8 77.8 81.2 84.9 81.6 78.7 64.8 61.1 84.4 71.8 92.5
    YOLOv7-tiny[45] 13.1 64.2 70.3 84.5 46.7 76.8 35.4 66.3 83.3 41.7 72.7
    YOLOv3-tiny[41] 5.57 63.2 65.2 73.6 69.4 63.2 37.5 63.9 76 48 72.1
    YOLOv5n[40] 4.2 72.3 86 79.2 74.5 77.9 59.3 52.8 81.8 58 80.8
    WFCCMNet 23.7 83.3 89.8 85.7 79.1 82.9 73.1 85.1 91.7 71.2 91
    下载: 导出CSV
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  • 收稿日期:  2024-02-05
  • 录用日期:  2024-04-26
  • 网络出版日期:  2024-05-17

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