Computer Vision Aided Pantograph Fault Identification Method for Multiple Units

In order to solve the technical requirements for automatic recognition and judgment of pantograph wear degree of Multiple Units, this paper designs a network structure based on Mask R-CNN structure. At the same time, in order to improve the ability of image feature extraction in the network, the original backbone network is replaced with ResNet-50, a residual network with more prominent feature extraction ability. Secondly, in order to improve the ability to search for targets in the image, the detection head is reconstructed, to improve the recognition ability of targets. Finally, the effectiveness of the algorithm and its ability to identify pantograph faults were verified through simulation experiments.