Research on road crack detection algorithm based on YOLO-SW

Efficient and highly accurate road crack detection algorithms are particularly important in road inspection systems. However, some of their limitations have gradually come to the fore as the target detection aspect has become more in-depth. Existing road target detection algorithms face the difficulty of capturing long-range dependencies, resulting in limited feature expressiveness and high leakage rates in small target detection scenarios (e.g., road fine crack identification). Therefore, in this paper, we propose an improved model YOLO-SW based on YOLO11n. Firstly, based on the structure of the C2f module, we form a deep feature processing chain by stacking n PMSFA modules through nn.ModuleList (the module is responsible for multiscale feature aggregation and attention mechanism), and propose the CSP_PMSFA module, which enhances the ability of small-target detection. The method replaces C3k2 of YOLO11n with the CSP_PMSFA module that enhances the feature expression ability by combining multi-scale feature aggregation and attention mechanism, which effectively improves the feature expression accuracy of small targets. At the same time, the CGAFusion module is added to enhance the feature expression ability by combining spatial attention, channel attention, and pixel attention.