In order to accurately identify different types of train connection hook tongues and ensure that the automatic re-hook robot adjusts the pose of the robot arm in real time according to the status of the hook tongues,a train connection hook tongue recognition method based on an improved YOLOv5 model was proposed.First,the original C3 module in the YOLOv5 backbone network was replaced with the C2F module(cross feature module)of rich gradient flow,and the original C3 module in the YOLOv5 neck network was replaced with the lightweight C3_FasterNet module based on the FasterNet block,and the CoordConv module was embedded at the end of the YOLOv5 backbone network.Second,the recognition test was carried out based on the spot measured image of the train connection hook tongue.The results show that the improved YOLOv5 algorithm can effectively improve the detection accuracy of the hook tongue target while reducing the numbers of model parameters.The identification accuracy of the hook tongue reaches 98.7％,and the numbers of model parameters are reduced by 10.8％compared with the original algorithm,which can provide an effective solution for the re-hook robot in the operation of hook tongue resetting and carriage connection.
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