Aiming at the problems of poor boundary recognition effect and low segmentation accuracy of rail transit wheel set tread damage image, an improved DeeplabV3+ algorithm is proposed to recognize and segment the damage area. Firstly, the lightweight network MobileNetV2 is used as the backbone feature extraction network to speed up the speed of semantic segmentation; Then, the expansion convolution in Atrous Spatial Pyramid Pooling module and the ordinary convolution after feature fusion are replaced by Deep Separable Convolution, so as to reduce the amount of parameters and reduce the complexity of the model; Finally, ECA mechanism is added to the shallow and deep feature layers of the backbone network output to strengthen the network feature learning ability and make the model more sensitive to the damaged area, so as to improve the segmentation accuracy of the model. The experimental results show that the size of the improved DeeplabV3+ model is reduced to 5%, the mPA value is 90.70%, and the mIou value is 84.33%. While the model is lighter, the segmentation effect of tread damage image is ensured.