Crack-YOLOv8: multi-class crack detection method for track slabs based on improved YOLOv8

Abstract Cracks in high-speed rail track slabs are important indicators of aging, and the issue of ineffective crack repairs remains a significant challenge in crack maintenance. However, research on this issue is limited. This paper proposes a novel multi-class crack detection method based on the YOLOv8 framework, addressing the problem of ineffective crack repairs. To enhance the model’s feature extraction capability, we introduce the dynamic snake convolution (DSC) module into the YOLOv8 backbone network and reconstruct the Bottleneck structure in C2f, establishing the C2f-DSC module. This modification replaces some of the C2f modules, enabling better multi-scale feature extraction, particularly for difficult-to-detect ineffective cracks. Additionally, we incorporate the CA attention mechanism in the neck network to improve the model’s ability to focus on critical features and effectively transmit subtle crack details throughout the network. We also replace the CIoU loss function with SIoU, reducing excessive penalization of geometric factors, thereby enhancing the model’s generalization ability. Finally, we validate the proposed method through a comprehensive evaluation of network structure, crack data, classification methods, and environmental conditions. Experimental results show that the proposed Crack-YOLOv8 model significantly improves detection accuracy, reducing both false positives and false negatives. Specifically, the average precision and recall are enhanced by 4.9% and 4.3%, respectively, demonstrating the effectiveness of our approach in accurately detecting ineffective crack repairs in rail track slabs.