中圖分類(lèi)號(hào)：TP391.41；U418.6 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.234577 中文引用格式： 王向前，成高立，胡鵬，等. 基于改進(jìn)YOLOv5的路面裂縫檢測(cè)方法[J]. 電子技術(shù)應(yīng)用，2024，50(3)：80-85. 英文引用格式： Wang Xiangqian，Cheng Gaoli，Hu Peng，et al. Pavement crack detection method based on improved YOLOv5[J]. Application of Electronic Technique，2024，50(3)：80-85.
Pavement crack detection method based on improved YOLOv5
Wang Xiangqian1，Cheng Gaoli1，Hu Peng2，Xia Xiaohua2
1.Shanxi Expressway Mechanization Engineering Limited Company， Xi′an 710038， China； 2.National Engineering Research Center of Highway Maintenance Equipment， Chang′an University， Xi′an， 710064，China
Abstract： 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.
Key words : road surface crack detection；YOLOv5；object detection；C3TR；CBAM；EIOU
