An improved YOLOv5s sleeper crack target detection algorithm was proposed, in response to the safety hazards posed by the increasing number of crack defects in high-speed rail sleepers due to extended service life, as well as the issues of missed and false detections of surface fine cracks in high-speed rail sleepers. In the backbone network of the YOLOv5s algorithm, the full-dimensional dynamic convolution based on the multi-dimensional attention mechanism was used instead of the traditional convolution to enhance the overall feature extraction ability of the network and improve the detection accuracy of fine cracks. An improved lightweight C3 structure was proposed based on the ConvNeXt module and depth-separable convolution to compress the model volume and accelerate the convergence of the network to improve the detection efficiency. The scale-optimized weighted GFPN feature fusion network was used to solve the problem of detail feature loss in the sampling process of small targets at multiple scales. The improved YOLOv5s sleeper crack target detection algorithm could solve the problem of missed detection of fine cracks on the sleeper surface effectively. The experimental results showed that the parameter count of the improved algorithm model was decreased by 19.7%, the accuracy rate, recall rate and mean average precision were increased by 1.8, 2.4 and 4.2 percentage points respectively, and the detection speed was up to 96 frames per second. The results verify that the proposed lightweight YOLOv5s-OCG algorithm model provides an effective solution for the real-time detection of surface cracks on sleepers.