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

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

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。

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.