Abstract: The 4C detection system of high-speed railway can obtain a large number of pictures of high-speed railway catenary. How to use artificial intelligence technologies to detect the looseness, dropping, deformation and other faults of catenary support devices is an urgent technical problem to be solved. Because the fasteners occupy a very small part of the whole images, a feasible solution to the problem is to identify and locate the fasteners first, then segment them, and finally identify the fault type of the segmented fasteners. Aiming at the problem of fastener identification and location, we propose an improved Faster R-CNN algorithm, which can accurately identify and locate various fasteners. The specific improvement strategy is to introduce an attention mechanism based on the SE model into the deep network, extract effective features from each channel, and use GA-RPN instead of RPN in the Faster R-CNN. The experimental results show that the method proposed in this paper has a recognition accuracy of more than 93.4%.

Key words: catenary fastener, identification and positioning, deep learning, Faster R-CNN, attention mechanism