关键词: 异物入侵检测, 高铁接触网, 直线检测, 深度学习, YOLOv3

Abstract: The contact net is a special form of transmission line that is erected along the railway line and supplies power to electric locomotive. The foreign objects such as the bird’s nest attached to the net cause safety hazards to train operation. At present, the contact net foreign matter is detected and removed by manual inspection. This method is not only costly and inefficient, but also cannot eliminate safety hazards in time. In order to timely and effectively detect foreign matter in the contact net and reduce labor cost, a real-time detection algorithm of railway intrusion foreign objects is proposed by using computer vision and deep learning techniques for the fixed structural features of high-speed rail operating environment. Firstly, based on the LSD straight line segment detection algorithm, the Region of Interest（ROI） that may appear in the nest is obtained. Secondly, the YOLOv3 network is used on ImageNet to obtain a pre-training weight, and the manually labeled data set is adopted to continue training the network until the network converges. Finally, the trained nest is applied to detect the nests in the ROI. The experimental results show that the average detection accuracy is 0.89 and the average detection speed is 38 f/s, which can achieve accurate real-time detection of foreign objects.

Key words: foreign object intrusion detection, high-speed rail contact net, line detection, deep learning, YOLOv3