Intelligent Detection Algorithm and Application of OCS Suspension Status in Urban Rail Transit
It is an important means to ensure the safe operation of OCS in urban rail transit by detecting the suspension state of OCS through high-definition imaging,and timely finding out defects such as looseness,jamming and wear of components and repairing them.The smallest version of YOLOV4-tiny is used as the basic model of the detection algorithm.The original Dropout layer is removed,and the upper-layer features are directly input to the lower convolutionallayer after passing through the convolutional layer.Mosaic data enhancement method is added to improve the generalization of the algorithm,and the original Leaky-Relu function is replaced with a smoother Mish function.The process of OCS defect locating is as follows:According to the improved YOLOv4-tiny algorithm training,a component area locating model is obtained for locating the position of components;then according to the improved YOLOv4-tiny algorithm training,a defect recognition model is obtained for judging whether the component has defects and giving the specific location and category of defects.Practical application shows that the algorithm can accurately and quickly locate the defect position of components,and give the type of defects with a total accuracy of above 90％.It greatly reduces the detection time and costs and ensures the safety of operation personnel.
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