Subtle defect detection on the surface of railway PCCS based on deep learning

The pantograph is a critical component of railway vehicles, and its operating status is directly related to railway operation safety. This paper concerns the detection of spall and crack on the surface of PCCS (Pantograph Carbon Contact Slide). For the detection of spall, EBF-YOLOv5 (YOLOv5 with Efficient Channel Attention, Bidirectional Feature Pyramid Network and Focal Loss) is put forward, which enhances the detection accuracy, reduces the omission rate and false detection rate of subtle spall on the surface of PCCS. For cracks segmentation, detect the crack at patch-level firstly, and then CBAMU-Net (Convolutional Block Attention Module U-Net) is presented to realize crack segmentation at pixel-level. The experimental results show it can effectively preserve the crack details to avoid over-segmentation and under-segmentation. In addition, the length and width of the crack are estimated on the segmented mask images, and the relative error is less than 7.1%. Experiments demonstrate that EBF-YOLOv5 and CBAMU-Net can meet the demand of practical application of typical PCCS subtle surface defects.