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- Title
Bullet Train Motion Video-Based Noise-Barrier Defects Inspection Method.
- Authors
Zhao, Hongwei; Xu, Huating; Li, Yidong; Dong, Rui; Liu, Junbo; Wang, Shengchun
- 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.
- Subjects
HIGH speed trains; DEEP learning; NOISE barriers; PROBLEM solving
- Publication
Journal of Circuits, Systems & Computers, 2023, Vol 32, Issue 1, p1
- ISSN
0218-1266
- Publication type
Article
- DOI
10.1142/S0218126623500044