Abstract
There are many kinds of pavement diseases, and the detection background in the actual scene is complex, so the accuracy of the detection model is not high. Based on YOLOv5s, a pavement diseases detection model YOLOv5s-CBP is proposed, and the CBAM (Convolutional Block Attention Module) attention module was introduced into the backbone network to enhance the model's attention to the characteristics of pavement diseases. The BiFPN network was constructed in the neck, which realizes the cross-layer exchange and integration of information and retains more high-level semantic information. A small target detection layer P2 was added to the detection head to improve the detection ability of the model for small road diseases. The experimental results on data sets show that the precision and recall of the model are improved by 6.1% and 4.4% respectively, and the mAP is improved by 5.7%, which effectively improves the performance of the detection model.
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