{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T04:14:24Z","timestamp":1727064864201},"reference-count":46,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"publisher","award":["2017YFF0205501"],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Key Research and Development Program","award":["2018C03035"]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61701467"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS-1555072","DMS-1736364","DMS-1821233"],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2019]]},"DOI":"10.1109\/access.2019.2958264","type":"journal-article","created":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T20:21:21Z","timestamp":1577132481000},"page":"182060-182077","source":"Crossref","is-referenced-by-count":83,"title":["Infrared Thermal Imaging-Based Crack Detection Using Deep Learning"],"prefix":"10.1109","volume":"7","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-1109-3380","authenticated-orcid":false,"given":"Jun","family":"Yang","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2804-5182","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0976-1987","authenticated-orcid":false,"given":"Guang","family":"Lin","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6073-1241","authenticated-orcid":false,"given":"Qing","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yeqing","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Yixuan","family":"Sun","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654889"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref33","first-page":"91","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","author":"ren","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2389824"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.314"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.03.030"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref34","first-page":"51","article-title":"Malicious software classification using VGG16 deep neural network’s bottleneck features","author":"rezende","year":"2018","journal-title":"Journal of Information Technology"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2013.03.006"},{"key":"ref40","doi-asserted-by":"crossref","first-page":"631","DOI":"10.3390\/rs11060631","article-title":"R-CNN-based ship detection from high resolution remote sensing imagery","volume":"11","author":"zhang","year":"2019","journal-title":"Remote Sens"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2014.11.004"},{"key":"ref12","article-title":"Unmanned aerial vehicle augmented bridge inspection feasibility study","author":"dorafshan","year":"2017"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2014.09.025"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2015.06.013"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2005.06.049"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/s10921-016-0344-x"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2017.11.007"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.sna.2016.06.028"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2016.2625815"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.firesaf.2007.05.002"},{"key":"ref4","first-page":"1096","article-title":"Enhanced performance for support vector machines as multi-class classifiers in steel surface defect detection","volume":"6","author":"amid","year":"2012","journal-title":"World Acad Sci Eng Technol"},{"key":"ref27","author":"dorafshan","year":"2017","journal-title":"Fatigue Crack Detection Using Unmanned Aerial Systems in Under-Bridge Inspection"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/978-3-642-25658-5_21","article-title":"Real-time steel inspection system based on support vector machine and multiple kernel learning","author":"chen","year":"2011","journal-title":"Practical Applications of Intelligent Systems"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.3390\/infrastructures3040045"},{"key":"ref29","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2019.03.010"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2008.919011"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.ndteint.2018.09.010","article-title":"A novel machine learning model for eddy current testing with uncertainty","volume":"101","author":"zhu","year":"2019","journal-title":"NDT&E Int"},{"key":"ref2","first-page":"239","article-title":"An intelligent real-time vision system for surface defect detection","volume":"3","author":"jia","year":"2004","journal-title":"Proc 17th Int Conf Pattern Recognit (ICPR)"},{"key":"ref9","first-page":"1","article-title":"Active thermography for the characterization of surfaces and interfaces of historic masonry structures","volume":"30","author":"maierhofer","year":"2009","journal-title":"Proc 7th Int Symp Non-Destructive Test Civil Eng (NDTCE)"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S1006-706X(14)60027-3"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2013.6728559"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2019.07.293"},{"key":"ref45","article-title":"Yolov3: An incremental improvement","author":"redmon","year":"2018","journal-title":"arXiv 1804 02767"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12375"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref42","article-title":"Revisiting small batch training for deep neural networks","author":"masters","year":"2018","journal-title":"arXiv 1804 07612"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12334"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1993.298623"},{"key":"ref23","first-page":"668","article-title":"Convolutional neural networks for steel surface defect detection from photometric stereo images","author":"soukup","year":"2014","journal-title":"Proc Int Symp Vis Comput"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/AIC-MITCSA.2016.7759946"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)BE.1943-5592.0001291"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CCE.2016.7562656"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.98"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/6287639\/8600701\/8928570-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8600701\/08928570.pdf?arnumber=8928570","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T18:53:48Z","timestamp":1649444028000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8928570\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":46,"URL":"https:\/\/doi.org\/10.1109\/access.2019.2958264","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]}}}