Abstract: As the operating time of a road increasing， the repeated effects of traveling loads and natural factors lead to deterioration of the road condition， and impacting its service life and quality. Therefore， In this paper， an improved YOLOv8 network is proposed for pavement disease detection. Firstly， targeted data enhancement techniques such as image flipping， lighting conditions change， and motion blur operation are applied， considering the characteristics of road disease images. Secondly， the loss function Wise-IoU is employed， which adopts a dynamic nonlinear focusing mechanism to evaluate the quality of the anchor box with outliers instead of IoU， and the wise gradient gain allocation strategy is provided to balance the differences in the number of samples among disease categories and improve the overall performance of the detector. Additionally， part of the C2F modules are replaced with DCNv3， and convolutional neuron weights are shared to reduce computational complexity and better learn features in pavement disease images. At the same time， multiple mechanisms are introduced， Softmax normalization along the sampling points enhances the model’s ability to understand road disease images. The experimental results show that the improved YOLOv8 road disease detection algorithm can achieve an accuracy of 77.3% in testing the network model， which is 3.9 percentage points higher than YOLOv8. mAP@50 reaches 76.9%， which is 3.4 percentage points higher than YOLOv8. This model can detect road diseases accurately and precisely， which is superior to the existing road disease detection algorithms and can applicate in engineering. 

Key words:  , pavement disease detection； YOLOv8 algorithm； object detection； Wise-IoU loss； deformable convolution v3