能否准确识别火车车钩的连接手柄对摘钩机器人的工作具有重大意义。针对这一问题，提出一种基于改进YOLOv5的火车连接手柄目标识别算法，在主干特征提取网络中融入C_SAC（C_switchable atrous convolution）模块，并引入WIOU （wise intersection over union）函数作为新的边界框损失函数，增强主干网络的特征提取能力，提高模型的泛化能力和收敛速率。然后采集生产现场不同环境、不同位置下的火车车钩连接手柄图片进行识别，实验结果表明改进的YOLOv5算法对火车车钩连接手柄的目标识别率达到96.6%，相比于原始算法效果在准确率、召回率、平均精度等方面均有较大提升。最后应用于研制的火车车厢自动摘钩机器人中，极大提高了自动摘钩的准确性和有效性。

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.