Fig. 1. Network structure of YOLOv4

Fig. 2. Schematic diagram of CSPResNet. (a) Resblock_body; (b) CSPResNet(X)t

Fig. 3. Standard convolution process

Fig. 4. Depth separable convolution process

Fig. 5. Standard convolution and depth separable convolution model based on YOLOv4 network structure. (a) Standard convolution model; (b) depth separate convolution model

Fig. 6. Examples of data set and label for road surface disease. (a) Examples of data set for road surface disease; (b) examples of label for road surface disease

Fig. 7. Examples of road surface disease data augmenting

Fig. 8. Changes of training losses based on improved YOLOv4

Fig. 9. Comparison of P-R curves for road surface disease based on different network models

Fig. 10. Detection results of road surface disease based on different network models. (a) Input images; (b) Faster R-CNN; (c) SSD; (d) YOLOv3; (e) YOLOv4; (f) YOLOv4+DC; (g) YOLOv4+FL; (h) YOLOv4+DC+FL