基于改进YOLOv5的路面裂缝检测方法  

Pavement crack detection method based on improved YOLOv5

作　　者：王向前 成高立 胡鹏[2] 夏晓华[2] Wang Xiangqian;Cheng Gaoli;Hu Peng;Xia Xiaohua(Shanxi Expressway Mechanization Engineering Limited Company,Xi′an 710038,China;National Engineering Research Center of Highway Maintenance Equipment,Chang′an University,Xi′an,710064,China)

机构地区：[1]陕西高速机械化工程有限公司,陕西西安710038 [2]长安大学公路养护装备国家工程研究中心,陕西西安710064

出　　处：《电子技术应用》2024年第3期80-85,共6页Application of Electronic Technique

基　　金：陕西省交通运输厅科研项目重点项目(23-10X)。

摘　　要：针对现有裂缝检测模型体积较大且检测精度不高的问题,提出一种基于轻量化网络的无人机航拍图像裂缝检测方法。首先,使用MobileNetv3网络替代YOLOv5的主干网络,降低模型大小;其次,引入C3TR和CBAM模块提高网络表征能力,将损失函数替换为EIOU以提高模型的鲁棒性。实验结果表明,该方法在自制数据集上获得98.9%的精度,相较于原始YOLOv5提高1.2%,模型大小减小51.5%,检测速度提高37%。改进后的模型在精度、大小和速度上均优于Faster-RCNN等4种常见裂缝检测模型,满足了裂缝检测的实时性、轻量化和精度需求。Aiming at the problem that the existing crack detection model is large in size and the detection accuracy is not high,this paper proposes a crack detection method for UAV aerial images based on lightweight network.Firstly,the MobileNetv3 network is used instead of the YOLOv5 backbone network to reduce the model size.Secondly,the C3TR and CBAM modules are introduced to improve the network characterization ability,and the loss function is replaced with EIOU to improve the robustness of the model.Experimental results show that the proposed method obtains 98.9%accuracy on the self-made dataset,which is 1.2%higher than the original YOLOv5,the model size is reduced by 51.5%,and the detection speed is increased by 37%.The improved model is superior to four common crack detection models such as Faster-RCNN in terms of accuracy,size and speed,which meets the real-time,lightweight and accuracy requirements of crack detection.

关 键 词：路面裂缝检测 YOLOv5 目标检测 C3TR CBAM EIOU 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] U418.6[自动化与计算机技术—计算机科学与技术]