面向探地雷达常见地下目标的GDS-YOLOv8n检测方法  

Detection of Common Underground Targets in Ground Penetrating Radar Images Using the GDS-YOLOv8n Model

作　　者：王惠琴[1] 杨发东 何永强[2] 刘宾灿 刘鑫 WANG Huiqin;YANG Fadong;HE Yongqiang;LIU Bincan;LIU Xin(School of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,China;School of Civil Engineering,Northwest Minzu University,Lanzhou 730030,China;SCEGC Installation Group Company LTD,Xi’an 710068,China)

机构地区：[1]兰州理工大学计算机与通信学院,兰州730050 [2]西北民族大学土木工程学院,兰州730030 [3]陕西建工安装集团有限公司,西安710068

出　　处：《雷达学报（中英文）》2024年第6期1170-1183,共14页Journal of Radars

基　　金：甘肃省重点研发计划(23YFFA0060);甘肃省优秀研究生“创新之星”项目。

摘　　要：针对当前探地雷达(GPR)图像检测中存在准确率低、误检和漏检等问题,该文提出了一种GPR常见地下目标检测模型GDS-YOLOv8n。该模型首先使用DRRB特征提取模块替换YOLOv8n模型中的部分C2f模块,旨在增强模型对多尺度特征的提取能力。其次使用SPD-Conv下采样模块替换像素为320×320及以下特征图所对应的Conv模块,有效克服分辨率受限以及存在小目标的GPR图像在下采样过程中的信息损失问题;同时利用辅助训练模块,在不增加检测阶段模型复杂度的前提下提升GPR图像的检测性能。最后,引入Inner-SIoU损失函数,在添加新约束条件的基础上,通过比例因子生成适合于当前GPR图像的辅助边界框,以提高预测框的准确性。实验结果表明,GDS-YOLOv8n模型对金属管、PVC管和电缆线等6类常见地下目标在实测GPR图像数据集上的P,R和mAP50分别为97.1%, 96.2%和96.9%,较YOLOv8n模型分别提高了4.0%, 6.1%和4.1%,尤其对PVC管和电缆线目标的检测效果提升更明显。与YOLOv5n, YOLOv7-tiny和SSD等模型相比,其mAP50分别提高了7.20%,5.70%和14.48%。此外,将GDS-YOLOv8n模型部署到NVIDIA Jetson Orin NX嵌入式设备上,并使用TensorRT进行优化。经FP16量化后,模型的检测速度由22.0 FPS提高到40.6 FPS,能够满足移动场景下GPR地下目标实时探测任务的需求。Ground Penetrating Radar(GPR)image detection currently faces challenges such as low accuracy,false detections,and missed detections.To overcome these challenges,we propose a novel model referred to as GDS-YOLOv8n for detecting common underground targets in GPR images.The model incorporates the DRRB(Dilated Residual Reparam Block)feature extraction module to achieve enhanced multiscale feature extraction,with certain C2f modules in the YOLOv8n architecture being effectively replaced.In addition,the space-todepth Conv downsampling module is used to replace the Conv modules corresponding to feature maps with a resolution of 320×320 pixels and less.This replacement assists in mitigating information loss during the downsampling of GPR images,particularly for images with limited resolution and small targets.Furthermore,the detection performance is enhanced using an auxiliary training module,ensuring performance improvement without increasing inference complexity.The introduction of the Inner-SIoU loss function refines bounding box predictions by imposing new constraints tailored to GPR image characteristics.Experimental results on realworld GPR datasets demonstrate the effectiveness of the GDS-YOLOv8n model.For six classes of common underground targets,including metal pipes,PVC pipes,and cables,the model achieves a precision of 97.1%,recall of 96.2%,and mean average precision at 50%IoU(mAP50)of 96.9%.These results indicate improvements of 4.0%,6.1%,and 4.1%,respectively,compared to corresponding values of the YOLOv8n model,with notable improvements observed when detecting PVC pipes and cables.Compared with those of models such as YOLOv5n,YOLOv7-tiny,and SSD(Single Shot multibox Detector),our model’s mAP50 is improved by 7.20%,5.70%,and 14.48%,respectively.Finally,the application of our model on a NVIDIA Jetson Orin NX embedded system results in an increase in the detection speed from 22 to 40.6 FPS after optimization via TensorRT and FP16 quantization,meeting the demands for the real-time detection of underground ta

关 键 词：探地雷达 深度学习 雷达图像处理 模型部署 实时检测 

分 类 号：TN957[电子电信—信号与信息处理] P631[电子电信—信息与通信工程]