Method for detecting residual thickness of electric collector shoe of metro linear motor vehicle based on deep learning

Abstract

At present, the wear of the electric collector shoe sliders of subway vehicles is mainly detected by naked eyes or simple rulers. There are problems such as low efficiency and low acquisition, and it is different to meet the needs of engineering applications. In this regard, this paper proposes a method for detecting the thickness of the electric collector shoe slider by the deep learning. First, establish the collector shoe slider dataset for the segmentation neural network method; second, use the attention mechanism and sub-pixel variable layer to optimize and train the original DeepLabV3 + neural network; finally, the optimized neural network and the traditional neural network method are used for comparative experts. The results show that the method proposed in this paper can significantly import the acquisition of the wear detection of the collector shoe slider.