A Defect-Detection Method of Split Pins in the Catenary Fastening Devices of High-Speed Railway Based on Deep Learning

Aiming at the fault states of the split pins in the catenary fastening devices of high-speed railway, such as missing, loosening, and improper installation, a method for detecting the defects in the split pins is proposed. First, the split pins are localized by a two-stage positioning method based on the improved YOLOV3 algorithm. The first stage is used to localize five joint components on the catenary support devices and the second stage is applied to locate the split pins in the joint component images. Then, the deeplabv3+ algorithm is implemented for semantic segmentation on the split-pin images. Finally, the split pins are classified based on the semantic segmentation images, in accordance with the split-pins' semantic information of the head, body, and tail. In order to verify the adaptability and accuracy of the proposed method, the catenary support device images in the multihigh-speed railway lines and multienvironments were tested. Meanwhile, our algorithm is compared with other deep learning algorithm. The results show that the proposed method has a higher accuracy in detecting the defects in the split pins, which can guarantee the stable operation of the catenary support devices.