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

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

侯斐斐¹,
彭应昊¹,
董健^2, ,,
银雪¹

1.
中南大学自动化学院长沙 410083
2.
中南大学电子信息学院长沙 410004

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
计量
- 文章访问数: 206
- HTML全文浏览量: 48
- PDF下载量: 30
- 被引次数: 0
出版历程
- 收稿日期: 2024-04-08
- 修回日期: 2024-09-12
- 网络出版日期: 2024-09-19

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

1.
School of Automation, Central South University, Changsha 410083, China
2.
School of Electronic Information, Central South University, Changsha 410004, China

Funds: The National Natural Science Foundation of China (62406346), Hunan Provincial Natural Science Foundation (2022J30052), Changsha Natural Science Foundation (kq2208285)

摘要

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。
- 探地雷达 /
- 目标检测 /
- 关键点检测 /
- YOLOv8
Abstract: Ground Penetrating Radar (GPR) is identified as a non-destructive method usable for the identification of underground targets. Existing methods often struggle with variable target sizes, complex image recognition, and precise target localization. To address these challenges, an innovative method is introduced that leverages a dual YOLOv8-pose model for the detection and precise localization of hyperbolic keypoint. This method, termed Dual YOLOv8-pose Keypoint Localization (DYKL), offers a sophisticated solution to the challenges inherent in GPR-based target identification and positioning. The proposed model architecture includes two-stages: firstly, the YOLOv8-pose model is employed for the preliminary detection of GPR targets, adeptly identifying regions that are likely to contain these targets. Secondly, building upon the training weights established in the first phase, the model further hones the YOLOv8-pose network. This refinement is geared towards the precise detection of keypoints within the candidate target features, thereby facilitating the automated identification and exact localization of underground targets with enhanced accuracy. Through comparison with four advanced deep-learning models— Cascade Region-based Convolutional Neural Networks (Cascade R-CNN), Faster Region-based Convolutional Neural Networks (Faster R-CNN), Real-Time Models for object Detection (RTMDet), and You Only Look Once v7(YOLOv7-face)—the proposed DYKL model exhibits an average recognition accuracy of 98.8%, surpassing these models. The results demonstrate the DYKL model’s high recognition accuracy and robustness, serving as a benchmark for the precise localization of subterranean targets.
- Ground penetrating radar /
- Target detection /
- Keypoint detection /
- YOLOv8

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图 1 算法整体框架

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图 2 YOLOv8-pose网络结构图

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图 3 关键点标注

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图 4 2维坐标系转换

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图 5 不同阶段下模型训练过程的损失曲线

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图 6 DYKL模型检测结果

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图 7 不同关键点检测算法在仿真图像上的性能对比

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图 8 不同关键点检测算法在实测图像数据上的对比结果

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图 9 与传统图像处理算法在仿真图像上的性能对比

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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

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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	32	64
图像尺寸	640×640	128×128

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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↑
DYKL	0.988	0.642	0.960
Cascade^[40]	0.962	0.597	0.675
Faster R-CNN^[17]	0.940	0.546	0.638
RTMDet^[41]	0.905	0.535	0.719

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