Lightweight Road Damage Detection Method Based on Improved YOLOv8.

Aiming at the problems of large memory space occupation, high computational complexity, and difficult to meet the real- time target detection requirements of the road damage detection model in complex scenes, a lightweight road damage detection model DGE-YOLO-P is proposed for the complex natural scenes. Firstly, the C2f fusion deformable convolutional design C2f_DCNv3 module in the network is enhanced to enhance the modelling capability of object deformation and the input feature information is dimensionality reduced to effectively reduce the number of parameters and the computational complexity. The input feature information is dimensionality reduced to effectively reduce the number of model parameters and computational complexity. Then, the GS-Decoupled head detection module is designed to reduce the parameters of the detection head while realising the effective aggregation of global information. At the same time, the E-Slide Loss weight function is designed to assign higher weights to the difficult samples, fully learn the difficult sample data in road damage, and further improve the model detection accuracy. Finally, channel pruning is used to reduce the redundant channels of the model, which effectively compresses the model volume and improves the detection speed. The experimental results show that the mAP of the DGE-YOLO-P model is increased by 2.4 percentage points compared with the YOLOv8n model, while the number of model parameters, computational volume and model size are reduced by 58.1%, 66.7% and 55.5%, respectively. The detection speed FPS is increased from 34 frame/s to 51 frame/s. [ABSTRACT FROM AUTHOR]