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基于双重YOLOv8-pose模型的探地雷达双曲线关键点检测与目标定位

侯斐斐 彭应昊 董健 银雪

侯斐斐, 彭应昊, 董健, 银雪. 基于双重YOLOv8-pose模型的探地雷达双曲线关键点检测与目标定位[J]. 电子与信息学报. doi: 10.11999/JEIT240242
引用本文: 侯斐斐, 彭应昊, 董健, 银雪. 基于双重YOLOv8-pose模型的探地雷达双曲线关键点检测与目标定位[J]. 电子与信息学报. doi: 10.11999/JEIT240242
HOU Feifei, PENG Yinghao, DONG Jian, YIN Xue. Ground Penetrating Radar Hyperbolic Keypoint Detection and Object Localization Based on Dual YOLOv8-pose Model[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240242
Citation: HOU Feifei, PENG Yinghao, DONG Jian, YIN Xue. Ground Penetrating Radar Hyperbolic Keypoint Detection and Object Localization Based on Dual YOLOv8-pose Model[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240242

基于双重YOLOv8-pose模型的探地雷达双曲线关键点检测与目标定位

doi: 10.11999/JEIT240242
基金项目: 国家自然科学基金(62406346),湖南省自然科学基金(2022JJ30052),长沙市自然科学基金(kq2208285)
详细信息
    作者简介:

    侯斐斐:女,讲师,研究方向为探地雷达图像解译、无损探测、深度学习、目标识别

    彭应昊:男,本科生,研究方向为图像识别与深度学习

    董健:男,教授,研究方向为天线、雷达信号处理、机器学习算法及其电磁应用

    通讯作者:

    董健 dongjian@csu.edu.cn

  • 中图分类号: TN957.51; TP391

Ground Penetrating Radar Hyperbolic Keypoint Detection and Object Localization Based on Dual YOLOv8-pose Model

Funds: The National Natural Science Foundation of China (62406346), Hunan Provincial Natural Science Foundation (2022J30052), Changsha Natural Science Foundation (kq2208285)
  • 摘要: 探地雷达(GPR)是一种可用于地下目标识别的无损检测方法。针对现有方法存在不同尺度目标兼容性差、复杂图像识别难度大、无法精确定位等问题,该文提出一种基于双重YOLO姿态模型(YOLOv8-pose)的GPR双曲线关键点检测与目标定位,命名为双重YOLO关键点定位方法(DYKL),用于地下目标的检测与精确定位。所提模型架构包含两个阶段:首先,第1阶段是基于YOLOv8-pose模型的GPR目标检测,以确定候选目标的位置;接着,第1阶段的部分训练权重被共享并传递到第2阶段,后者以此为基础继续训练YOLOv8-pose网络,用于候选目标特征的关键点检测及获取,从而实现地下目标的自动化定位。通过与级联区域卷积网络(Cascade R-CNN)、 更快的区域卷积网络(Faster R-CNN)、 实时对象检测模型(RTMDet)以及“你只看一次”人脸模型(YOLOv7-face)4种先进的深度模型进行比较,所提模型平均识别准确率达到98.8%,性能优于其他模型。结果表明所提DYKL模型具有较高的识别准确性与较强的鲁棒性,可以为地下目标的精确定位提供参考。
  • 图  1  算法整体框架

    图  2  YOLOv8-pose网络结构图

    图  3  关键点标注

    图  4  二维坐标系转换

    图  5  不同阶段下模型训练过程的损失曲线

    图  6  DYKL模型检测结果

    图  7  不同关键点检测算法在仿真图像上的性能对比

    图  8  不同关键点检测算法在实测图像数据上的对比结果

    图  9  与传统图像处理算法在仿真图像上的性能对比

    表  1  GPR目标自动识别算法

    文献 模型类别 算法 检测目标 数据集 性能指标(%)
    兰天等人[34] 图像学目标检测 基于拟合误差消除双曲线识别模型 / 实测图像和仿真图像 /
    Pham等人[16] 两阶段目标检测 Faster R-CNN / 50张仿真图像和100张实测图像 /
    Cui等人[18] 两阶段目标检测 Faster R-CNN 地层界面 / 检测框Pre= 98.3
    Zhao等人[20] 两阶段实例分割 Mask R-CNN 隧道衬砌上缺陷位置 / 检测框Pre=96.1
    掩模预测Pre=95.6
    Hou等人[21] 两阶段实例分割 基于MS—RCNN定制锚定方案 树根 95张实测图像 掩膜预测AP50=38.6
    Wang等人[26] 单阶段目标检测 SSD引入FPN特征融合层网络和广义交并集损失 / 1 170张仿真图像 检测框Pre=92.0
    检测框Rec=90.3
    Qiu等人[28] 单阶段目标检测 YOLOv5 铁实验材料 412张实测图像 检测框Pre=82.6
    检测框Rec=68.0
    Hu等人[29] 单阶段目标检测 YOLOv5引入注意力机制 地下缺陷 3 256张图像包含实测图像和仿真图像 检测框mAP=85.4
    胡荣明等人[30] 单阶段目标检测 YOLOv7 隧道衬砌病害 506张仿真图像和84张实测图像 检测框Pre=97.9
    检测框Rec=90.6
    Wang等人[31] 单阶段目标检测 YOLOv8引入CBAM注意力机制 地下缺陷 837张实测图像 检测框mAP50=90.8
    检测框F1=88.3
    Li等人[32] 单阶段关键点检测 YOLOv4引入关键点回归并增加Wing损失函数 植物根 1 000张仿真图像和2 320张实测图像 检测框Pre=94.0
    检测框Rec=96.7
    方涛涛等人[33] 单阶段关键点检测 YOLOv8引入 CBAM 注意力机制和关键点回归 地下管线 545张实测图像和795张仿真图像 定位水平误差8.6
    定位深度误差1.8
    下载: 导出CSV

    表  2  模型输入图像的数量与尺寸

    参数目标检测阶段关键点检测阶段
    批量大小3264
    图像尺寸640×640128×128
    下载: 导出CSV

    表  3  各种目标检测算法的平均精度与平均召回率

    模型mAP50↑mAP50-95↑Av_Recall↑
    DYKL0.9880.6420.960
    Cascade[40]0.9620.5970.675
    Faster R-CNN[17]0.9400.5460.638
    RTMDet[41]0.9050.5350.719
    下载: 导出CSV
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  • 收稿日期:  2024-04-08
  • 修回日期:  2024-09-13
  • 网络出版日期:  2024-09-19

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