Road Damage Detection Method based on Improved YOLO-World

Artificial Intelligence AI -driven road damage detection is a crucial component of intelligent transportation and smart cities. Given the outstanding performance of the You Only Look Once YOLO model in computer vision tasks, intelligent road damage detection technologies based on the YOLO model are currently among the mainstream methods. However, existing methods have limitations such as low accuracy and poor real-time performance when dealing with small objects and complex backgrounds. To alleviate these issues, this paper proposes an intelligent road damage detection method based on an improved YOLO-World model. Firstly, the Spatial Pyramid Pooling Cross Stage Partial Channel SPPCSPC convolutional structure and the FasterNet architecture are introduced into the detection backbone of the YOLO-World model. The aim is to simultaneously enhance the model's ability to extract multi-scale features and its detection speed. Secondly, the Convolutional Block Attention Module CBAM attention mechanism module is introduced into the detection head to improve the model's ability to extract key features. Finally, experimental results on the constructed complex road damage dataset show that the improved YOLO-World model outperforms existing state-of-the-art methods in terms of accuracy and detection speed. In particular, the mAP50 index of the improved model is 18.2 percentage points higher than that of YOLOv10.