Research On Defect Detection Method Of Split Pins In The Catenary Fastening Devices Of High-Speed Railway Based On Deep Learning

The catenary structure of high-speed railway plays an important role in the power supply system of high-speed railway.Moreover,there is no standby device on the running line,and the violent vibration brought by the high-speed train and bad weather also bring hidden dangers to the safe operation of the catenary.Therefore,to ensure the safety of high-speed rail power supply,timely and effective monitoring and testing of catenary is needed.At present,the detection methods of catenary on railway mainly include manual inspection,manual viewing of images by workers and analysis of images by computer vision algorithm.Using image processing algorithm has gradually become an efficient catenary detection method,but there is still much room for improvement.Aiming at the fault states of split pins in the catenary fastening devices of highspeed railway,such as missing,loosening and improper installation,a three-stage method for detecting defects of split pins is proposed based on the research and analysis of the structural distribution of catenary components and the current progress of defect detection algorithms for catenary components.This method is based on object detection algorithm,semantic segmentation algorithm and morphological processing method.And the effectiveness of the algorithm proposed in this paper is verified by experimental tests.Firstly,split pins are localized by a two-stage positioning method based on the YOLOV4 algorithm.The first stage is used to localize five joint components on catenary support devices and the second stage is applied to locate the split pins in the joint component images.In this way,the problem of inaccurate positioning due to the small proportion of split pins in the catenary picture is solved.Then,the deeplabv3+ algorithm is implemented for semantic segmentation on split pin images.As a kind of deep learning algorithm,semantic segmentation algorithm can effectively distinguish object categories and form corresponding semantic regions.The semantic segmentation algorithm adopted in this paper is mainly used to extract the semantic information of split pins in catenary.Finally,split pins are classified based on semantic segmentation images,in accordance with split pins’ semantic information of both head,body and tail.In order to verify the adaptability and accuracy of the proposed method,the catenary support devices images in multi high-speed railway lines and multi environments 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 defects of split pins,which can guarantee the stable operation of catenary support devices.At the same time,it also shows that the application of deep learning algorithm can complete the inspection of catenary more accurately and efficiently,so it can also be applied to the defect inspection of other parts of catenary.