{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T16:02:32Z","timestamp":1769270552638,"version":"3.49.0"},"reference-count":49,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":["51977182"],"award-info":[{"award-number":["51977182"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Instrum. Meas."],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tim.2023.3307181","type":"journal-article","created":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T17:48:48Z","timestamp":1692640128000},"page":"1-14","source":"Crossref","is-referenced-by-count":9,"title":["ECF-STPM: A Robust Crack Detection Method for Railway Catenary Components"],"prefix":"10.1109","volume":"72","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2578-4908","authenticated-orcid":false,"given":"Yujing","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3830-0722","authenticated-orcid":false,"given":"Junping","family":"Zhong","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4154-5587","authenticated-orcid":false,"given":"Zhigang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4322-208X","authenticated-orcid":false,"given":"Zhiwei","family":"Han","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","first-page":"1","article-title":"Fast image recognition of transmission tower based on big data","volume":"3","author":"hu","year":"2018","journal-title":"Protection Control Modern Power Syst"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"11162","DOI":"10.1109\/TITS.2021.3101053","article-title":"MRSDI-CNN: Multi-model rail surface defect inspection system based on convolutional neural networks","volume":"23","author":"zhang","year":"2022","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2018.07.017"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2019.2905905"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3085766"},{"key":"ref10","first-page":"21","article-title":"SSD: Single shot MultiBox detector","author":"liu","year":"2016","journal-title":"Computer Vision (ECCV)"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3020287"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2019.2941292"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref18","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention (MICCAI)"},{"key":"ref46","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1117\/1.1636183","article-title":"Retinex processing for automatic image enhancement","volume":"13","author":"rahman","year":"2004","journal-title":"J Electron Imag"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00555"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3055564"},{"key":"ref41","article-title":"Student&#x2013;teacher feature pyramid matching for anomaly detection","author":"wang","year":"2021","journal-title":"arXiv 2103 04257"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3051462"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2018.01.010"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00954"},{"key":"ref8","first-page":"1","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"28","author":"ren","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2020.107336"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2020.2987457"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2019.2954757"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/PESA.2017.8277746"},{"key":"ref40","article-title":"Sub-image anomaly detection with deep pyramid correspondences","author":"cohen","year":"2020","journal-title":"arXiv 2005 02357"},{"key":"ref35","article-title":"Improving unsupervised defect segmentation by applying structural similarity to autoencoders","author":"bergmann","year":"2018","journal-title":"arXiv 1807 02011"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00982"},{"key":"ref37","first-page":"1","article-title":"Lossy image compression with compressive autoencoders","author":"theis","year":"2017","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref36","first-page":"622","article-title":"GANomaly: Semi-supervised anomaly detection via adversarial training","author":"akcay","year":"2018","journal-title":"Proc Asian Conf Comput Vis"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412842"},{"key":"ref30","first-page":"1","article-title":"Superpixel masking and inpainting for self-supervised anomaly detection","author":"li","year":"2020","journal-title":"Proc BMVC"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58565-5_22"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2022.3205452"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2023.3243024"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00424"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3113121"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3117357"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.10.107"},{"key":"ref25","first-page":"43","article-title":"Research on crack detection of catenary cable base of high-speed railway based on accelerated region convolution neural network","volume":"41","author":"kai","year":"2019","journal-title":"J China Railway Soc"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00925"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3086913"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2955753"},{"key":"ref28","first-page":"4393","article-title":"Deep one-class classification","author":"ruff","year":"2018","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3165575"},{"key":"ref29","first-page":"375","article-title":"Patch SVDD: Patch-level SVDD for anomaly detection and segmentation","author":"yi","year":"2020","journal-title":"Proc Asian Conf Comput Vis"}],"container-title":["IEEE Transactions on Instrumentation and Measurement"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/19\/10012124\/10225612.pdf?arnumber=10225612","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T18:15:09Z","timestamp":1695665709000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10225612\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":49,"URL":"https:\/\/doi.org\/10.1109\/tim.2023.3307181","relation":{},"ISSN":["0018-9456","1557-9662"],"issn-type":[{"value":"0018-9456","type":"print"},{"value":"1557-9662","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}