{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:03:53Z","timestamp":1753884233501,"version":"3.41.2"},"reference-count":38,"publisher":"World Scientific Pub Co Pte Ltd","issue":"01","funder":[{"name":"Key Research and Development Project of China Academy of Railway Sciences Corporation Limited","award":["2021YJ310"],"award-info":[{"award-number":["2021YJ310"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2023,1,15]]},"abstract":"<jats:p> 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. <\/jats:p>","DOI":"10.1142\/s0218126623500044","type":"journal-article","created":{"date-parts":[[2022,7,9]],"date-time":"2022-07-09T02:19:40Z","timestamp":1657333180000},"source":"Crossref","is-referenced-by-count":0,"title":["Bullet Train Motion Video-Based Noise-Barrier Defects Inspection Method"],"prefix":"10.1142","volume":"32","author":[{"given":"Hongwei","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, P.\u00a0R.\u00a0China"}]},{"given":"Huating","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, P.\u00a0R.\u00a0China"}]},{"given":"Yidong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, P.\u00a0R.\u00a0China"}]},{"given":"Rui","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1782-1051","authenticated-orcid":false,"given":"Junbo","family":"Liu","sequence":"additional","affiliation":[{"name":"Infrastructure Inspection Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing, P.\u00a0R.\u00a0China"}]},{"given":"Shengchun","family":"Wang","sequence":"additional","affiliation":[{"name":"Infrastructure Inspection Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing, P.\u00a0R.\u00a0China"}]}],"member":"219","published-online":{"date-parts":[[2022,8,10]]},"reference":[{"key":"S0218126623500044BIB001","doi-asserted-by":"crossref","first-page":"6844","DOI":"10.1109\/JSEN.2019.2911015","volume":"16","author":"Liu J. 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