Accurately identifying the connecting handle of the train coupler is of great significance for the operation of the uncoupling robot. A train connection handle target recognition algorithm based on improved YOLOv5 was proposed to address this issue. The C_switchable atrous convolution(C_SAC) module was integrated into the backbone feature extraction network, and the wise intersection over union(WIOU) function was introduced as a new bounding box loss function to enhance the feature extraction ability of the backbone network, improve the model's generalization ability and convergence rate. Then, images of the connecting handles of train couplers in different environments and positions on the production site were collected for recognition. The experimental results show that the improved YOLOv5 algorithm achieves a target recognition rate of 96.6% for the connecting handles of train couplers. Compared with the original algorithm, it shows significant improvements in accuracy, recall, average accuracy, and other aspects. Finally, it was applied in the development of an automatic uncoupling robot for train carriages, greatly improving the accuracy and effectiveness of automatic uncoupling.