Bullet Train Motion Video-Based Noise-Barrier Defects Inspection Method

Abstract

Vision-based automatic noise-barrier inspection of high-speed railway, instead of manual patrol, remains a great challenge. Even though many supervised learning-based methods have been developed, massive redundant video frames and scarce defective samples are the main obstacles to leverage the performance of the noise-barrier inspection task. To tackle the problems, we present a novel Vision-based Noise-barrier Inspection System (VNIS), which is deployed on the bullet train to inspect the noise-barrier defects by using motion video. VNIS uses the proposed panorama generation model based on motion video to obtain panoramic images from massive redundant video sequences. Then, we employ a self-supervised learning deep network to solve the problem of the scarce defective samples. Comprehensive experiments are conducted on a large-scale video dataset of bullet train. VNIS yields competitive performance on noise-barrier defects inspection. Specifically, an average accuracy of 99.14% is achieved for noise-barrier defects inspection.