Improved YOLO v5 for Railway PCCS Tiny Defect Detection

Abstract

Pantograph defect of rolling stocks is directly related to its operation safety, so timely detection of its health status is one of the most important tasks in rolling stocks maintenance. In order to achieve rapid and accurate detection of PCCS (Pantograph Carbon Contact Strip) tiny defect, this paper puts forward an improved YOLO v5 model, in which Focal Loss function is applied. Besides, four-head structure is designed to retain more shallow features and the original PANet is replaced with BiFPN to achieve cross-scale feature fusion. After that, comparative experiments are conducted on self-made dataset. The results shows that our method improves the detection accuracy of tiny targets and reduces the false positive rate. The mAP@0.5 reaches 99.9% and Recall is 95.4%, while FPS reaches 196, which means our model can fully meet the requirement of real-time precise tiny detect detection.