{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T14:30:13Z","timestamp":1762353013393,"version":"3.37.3"},"reference-count":23,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100004489","name":"Mitacs","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004489","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,12,1]]},"DOI":"10.1109\/ssci47803.2020.9308472","type":"proceedings-article","created":{"date-parts":[[2021,1,5]],"date-time":"2021-01-05T23:12:38Z","timestamp":1609888358000},"page":"1328-1333","source":"Crossref","is-referenced-by-count":7,"title":["Optimized Deep Neural Network Architectures with Anchor Box optimization for Shipping Container Corrosion Inspection"],"prefix":"10.1109","author":[{"given":"Zhila","family":"Bahrami","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ran","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rakiba","family":"Rayhana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Teng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2012.11.003"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-31439-1_19"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1061\/9780784413029.086"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2016.08.008"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2017.04.005"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.18287\/1613-0073-2018-2212-186-192"},{"key":"ref16","first-page":"775","article-title":"Material corrosion classification based on deep learning","volume":"71","author":"zhao","year":"2018","journal-title":"Chemical Engineering Transactions"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1117\/1.JEI.28.4.043019"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/UKRCON.2019.8879804"},{"key":"ref19","first-page":"55","article-title":"A survey of computer vision based corrosion detection approaches","author":"ahuja","year":"2017","journal-title":"International Conference on Information and Communication Technology for Intelligent Systems"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-27149-1_18"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/6520620"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/108386"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.corsci.2004.05.007"},{"key":"ref8","article-title":"Detection of pitting corrosion in steel using image processing","author":"ghosh","year":"2010","journal-title":"2nd International Conference on Applications Heritage and Constructions in Coastal and Marine Environment MEDACHS 2010"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1155\/2010\/817473"},{"key":"ref2","article-title":"Road damage detection using deep neural networks with images captured through a smartphone","author":"maeda","year":"2018","journal-title":"arXiv preprint arXiv 1801 09030"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/s41529-018-0058-x"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.proeng.2011.07.058"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/6765274"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref21","first-page":"91","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","author":"ren","year":"2015","journal-title":"Advances in neural information processing systems"},{"key":"ref23","article-title":"Learn to pay attention","author":"jetley","year":"2018","journal-title":"arXiv preprint arXiv 1804 02671"}],"event":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","start":{"date-parts":[[2020,12,1]]},"location":"Canberra, ACT, Australia","end":{"date-parts":[[2020,12,4]]}},"container-title":["2020 IEEE Symposium Series on Computational Intelligence (SSCI)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9308061\/9308107\/09308472.pdf?arnumber=9308472","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T15:18:14Z","timestamp":1656602294000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9308472\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,1]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/ssci47803.2020.9308472","relation":{},"subject":[],"published":{"date-parts":[[2020,12,1]]}}}