Aiming at the problems of low detection accuracy, slow detection rate and single detection category of wheelset tread defects, an improved lightweight YOLOv7-tiny tread damage detection method is proposed. In this method, the lightweight MobileNetV3 network is used to replace the backbone network of YOLOv7-tiny, and the parameter number and calculation amount of the model are reduced. Embedding BiFormer attention mechanism into the backbone network can strengthen the features of the learning target region and improve the detection accuracy of the model. The centralized feature pyramid (CFP) is used to enhance the feature′s in-layer adjustment ability, capture the global long distance dependence and local critical information of tread defects. Wise intersection over union (WIoU) loss function is employed to accelerate the convergence rate of border regression loss and enhance the robustness of the model. GSconv decoupled head (GSDH) is introduced into the YOLOv7-tiny detection header to decouple separated feature channels from classification and regression tasks, effectively improving the parallel computation rate and detection accuracy of the model. The experimental results show that the improved YOLOv7-tiny network parameter number and computation amount are reduced by 11.7% and 21.2% respectively, the detection precision is increased by 7.9%, the recall rate is increased by 10.5%, the mean average precision is increased by 10.1%, and the frame per second is increased by 7.4 frames/s, which realizes lightweight and has better detection performance. The improved method has a wide application prospect in wheelset tread damage detection.