{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:51:24Z","timestamp":1773773484804,"version":"3.50.1"},"reference-count":37,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["91738301"],"award-info":[{"award-number":["91738301"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61803022"],"award-info":[{"award-number":["61803022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61833002"],"award-info":[{"award-number":["61833002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Project of Beijing Shanghai High Speed Railway Company: Research on Key Technologies of Unmanned Aerial Vehicles (UAV) Inspection for Beijing Shanghai High-Speed Railway Infrastructure","award":["I20D00010"],"award-info":[{"award-number":["I20D00010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Intell. Transport. Syst."],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1109\/tits.2021.3134318","type":"journal-article","created":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T20:30:41Z","timestamp":1639773041000},"page":"14806-14819","source":"Crossref","is-referenced-by-count":28,"title":["Fully Decoupled Residual ConvNet for Real-Time Railway Scene Parsing of UAV Aerial Images"],"prefix":"10.1109","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8564-7502","authenticated-orcid":false,"given":"Lei","family":"Tong","sequence":"first","affiliation":[{"name":"State Key Laboratory of Railway Traffic Control and Safety and the Key Laboratory of Railway Industry of Proactive Safety and Risk Control, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3039-7582","authenticated-orcid":false,"given":"Zhipeng","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Railway Traffic Control and Safety and the Key Laboratory of Railway Industry of Proactive Safety and Risk Control, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2161-4637","authenticated-orcid":false,"given":"Limin","family":"Jia","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Railway Traffic Control and Safety and the Key Laboratory of Railway Industry of Proactive Safety and Risk Control, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6519-8316","authenticated-orcid":false,"given":"Yong","family":"Qin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Railway Traffic Control and Safety and the Key Laboratory of Railway Industry of Proactive Safety and Risk Control, Beijing Jiaotong University, Beijing, China"}]},{"given":"Yanbin","family":"Wei","sequence":"additional","affiliation":[{"name":"93787 Army, Beijing, China"}]},{"given":"Huaizhi","family":"Yang","sequence":"additional","affiliation":[{"name":"Beijing-Shanghai High Speed Railway Company Ltd., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8942-2562","authenticated-orcid":false,"given":"Yixuan","family":"Geng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Railway Traffic Control and Safety and the Key Laboratory of Railway Industry of Proactive Safety and Risk Control, Beijing Jiaotong University, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.22190\/FUME190507041B"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.3390\/s20185220"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3390\/app8071028"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.06.076"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.cja.2020.02.024"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2750080"},{"key":"ref7","first-page":"1","article-title":"Speeding up semantic segmentation for autonomous driving","volume-title":"Proc. 29th Conf. Neural Inf. Process. Syst.","author":"Treml"},{"key":"ref8","article-title":"Pyramid scene parsing network","author":"Zhao","year":"2016","journal-title":"arXiv:1612.01105"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00161"},{"key":"ref10","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"Proc. ICLR","author":"Simonyan"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.48550\/arxiv.1706.05587"},{"key":"ref15","first-page":"1","article-title":"Semantic image segmentation with deep convolutional nets and fully connected CRFs","volume-title":"Proc. ICLR","author":"Chen"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref17","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017","journal-title":"arXiv:1704.04861"},{"key":"ref18","article-title":"DecomposeMe: Simplifying ConvNets for end-to-end learning","author":"Alvarez","year":"2016","journal-title":"arXiv:1606.05426"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2343229"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.5244\/C.28.88"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref22","article-title":"ENet: A deep neural network architecture for real-time semantic segmentation","author":"Paszke","year":"2016","journal-title":"arXiv:1606.02147"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_25"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_20"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00975"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00959"},{"key":"ref27","article-title":"Deep dual-resolution networks for real-time and accurate semantic segmentation of road scenes","author":"Hong","year":"2021","journal-title":"arXiv: 2101.06085v2"},{"key":"ref28","article-title":"YOLOv3: An incremental improvement","author":"Redmon","year":"2018","journal-title":"arXiv:1804.02767"},{"key":"ref29","article-title":"YOLOv4: Optimal speed and accuracy of object detection","author":"Bochkovskiy","year":"2020","journal-title":"arXiv:2004.10934"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.87"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"ref35","first-page":"1","article-title":"Multi-scale context aggregation by dilated convolutions","volume-title":"Proc. ICLR","author":"Yu"},{"key":"ref36","article-title":"Improving neural networks by preventing co-adaptation of feature detectors","author":"Hinton","year":"2012","journal-title":"arXiv:1207.0580"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"}],"container-title":["IEEE Transactions on Intelligent Transportation Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6979\/9893028\/09655444.pdf?arnumber=9655444","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:38:40Z","timestamp":1704847120000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9655444\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9]]},"references-count":37,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tits.2021.3134318","relation":{},"ISSN":["1524-9050","1558-0016"],"issn-type":[{"value":"1524-9050","type":"print"},{"value":"1558-0016","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9]]}}}