摘要
道路裂缝和坑洞的检测是道路安全检查中的重要部分。针对道路实时检测中存在的漏检、错检等问题,提出一种基于改进YOLOv7的道路裂缝和坑洞检测算法。先将裂缝分为纵向、横向和网状裂缝,再使用可变形卷积(deformable convolutional networks,DCN)替换原YOLOv7中特征提取网络里的卷积,使得形状差异较大且不规则的裂缝形状特征得到完整提取,提升裂缝的准确度;针对获取的图像中坑洞目标较小不易发现的问题,通过先将边界框建模为高斯分布,再使用基于Wasserstein距离(normalized Wasserstein distance,NWD)的新的度量标准的小目标检测评估方法,提高坑洞的检测精度。实验结果表明,改进后的算法较原YOLOv7算法检测精度提高了4.1%,同时检测速度提高了17%,表现出更出色的检测效果。
The detection of road cracks and potholes is an important part of road safety inspection.To address the problems of leakage and error detection in real-time road inspection,an improved YOLOv7 algorithm model was proposed.The cracks were first divided into longitudinal,transverse and alligator cracks,and then the deformable convolutional networks(DCN) was used to replace the convolution in the original YOLOv7 feature extraction network,so that the shape features of cracks with large differences in shape and irregularity were completely extracted and the accuracy of cracks was improved.The crater targets in the acquired images were generally small and not easily detected.The detection accuracy of potholes was improved by first modeling the bounding box as a Gaussian distribution,and then using a new metric based on Wasserstein distance(NWD) for small target detection evaluation method.The experimental results show that the improved algorithm improves the detection accuracy by 4.1% compared with the original YOLOv7 algorithm,while the detection speed increases by 17%,showing a better detection effect.
作者
宣以国
余成波
蒋启超
龚欣
XUAN Yi-guo;YU Cheng-bo;JIANG Qi-chao;GONG Xin(School of Electrical and Electronic Engineering,Chongqing University of Technology,Chongqing 400054,China)
出处
《科学技术与工程》
北大核心
2024年第17期7205-7213,共9页
Science Technology and Engineering
基金
重庆市自然科学基金(cstc2021jcyj-msxmX0251)
重庆市教育委员会科学技术研究计划(KJQN202101115,KJQN202201157)。
