{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:40:16Z","timestamp":1774485616556,"version":"3.50.1"},"reference-count":41,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T00:00:00Z","timestamp":1630972800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T00:00:00Z","timestamp":1630972800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T00:00:00Z","timestamp":1630972800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,7]]},"DOI":"10.1109\/etfa45728.2021.9613298","type":"proceedings-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:13:36Z","timestamp":1638317616000},"page":"1-7","source":"Crossref","is-referenced-by-count":3,"title":["Anomaly Detection for the Automated Visual Inspection of PET Preform Closures"],"prefix":"10.1109","author":[{"given":"Oliver","family":"Rippel","sequence":"first","affiliation":[]},{"given":"Peter","family":"Haumering","sequence":"additional","affiliation":[]},{"given":"Johannes","family":"Brauers","sequence":"additional","affiliation":[]},{"given":"Dorit","family":"Merhof","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","article-title":"Transfusion: Understanding transfer learning for medical imaging","volume":"32","author":"raghu","year":"0","journal-title":"Advances in neural information processing systems"},{"key":"ref38","first-page":"107706","article-title":"Reconstruction by inpainting for visual anomaly detection","author":"zavrtanik","year":"2020","journal-title":"Pattern Recognition"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/S0047-259X(03)00096-4"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref31","first-page":"1","article-title":"Autoencoder-based fabric defect detection with crosspatch similarity","author":"tian","year":"0","journal-title":"2019 16th International Conference on Machine Vision Applications (MVA) MVA"},{"key":"ref30","first-page":"7324","article-title":"Making convolutional networks shift-invariant again","author":"zhang","year":"0","journal-title":"Int Conference on Machine Learning"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.09.107"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ETFA46521.2020.9212099"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00424"},{"key":"ref34","first-page":"657","article-title":"A coherent interpretation of AUC as a measure of aggregated classification performance","author":"ferri","year":"0","journal-title":"Proceedings of the 28th International Conference on Machine Learning (ICML-11)"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/MMAR.2016.7575322"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01145"},{"key":"ref11","first-page":"3525","article-title":"Pet preform defect detection based on ransac and uniformity of grey level","volume":"29","author":"yin","year":"2012","journal-title":"Jisuanji Yingyong Yanjiu"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1117\/12.220917"},{"key":"ref13","first-page":"175","article-title":"Non-contact techniques for the quality analysis of pet bottles","author":"tarabini","year":"0","journal-title":"14th IMEKO TC10 Workshop on Technical Diagnostics 2016 New Perspectives in Measurements Tools and Techniques for Systems Reliability Maintainability and Safety"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICARCV.2004.1468949"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/S0734-189X(88)80033-1"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2018.2886977"},{"key":"ref17","author":"gonzalez","year":"2008","journal-title":"Digital Image Processing"},{"key":"ref18","first-page":"4393","article-title":"Deep one-class classification","volume":"80","author":"ruff","year":"2018","journal-title":"Proceedings of the 35th International Conference on Machine Learning ser Proceedings of Machine Learning Research"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00277"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1006\/cviu.1995.1017"},{"key":"ref27","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume":"97","author":"tan","year":"2019","journal-title":"Proceedings of the 36th International Conference on Machine Learning ICML 2019"},{"key":"ref3","article-title":"Visual inspection: a review of the literature","author":"see","year":"2012","journal-title":"Sandia National Laboratories"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/1541880.1541882"},{"key":"ref29","author":"melas-kyriazi","year":"2020","journal-title":"Eficientnet for pytorch"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01400-4"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3052449"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2013.12.026"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICECS.1999.813221"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412109"},{"key":"ref1","author":"aft","year":"1997","journal-title":"Fundamentals of Industrial Quality Control"},{"key":"ref20","article-title":"Sub-image anomaly detection with deep pyramid correspondences","author":"cohen","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref22","article-title":"Explainable deep one-class classification","author":"liznerski","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref21","article-title":"Padim: a patch distribution modeling framework for anomaly detection and localization","author":"defard","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref24","article-title":"Understanding the effective receptive field in deep convolutional neural networks","volume":"29","author":"luo","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref41","article-title":"Emerging properties in self-supervised vision transformers","author":"caron","year":"2021","journal-title":"ArXiv Preprint"},{"key":"ref23","article-title":"Rethinking assumptions in deep anomaly detection","author":"ruff","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref26","article-title":"Deep anomaly detection with outlier exposure","author":"hendrycks","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/456"}],"event":{"name":"2021 IEEE 26th International Conference on Emerging Technologies and Factory Automation (ETFA)","location":"Vasteras, Sweden","start":{"date-parts":[[2021,9,7]]},"end":{"date-parts":[[2021,9,10]]}},"container-title":["2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9613137\/9613141\/09613298.pdf?arnumber=9613298","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T16:52:31Z","timestamp":1652201551000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9613298\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,7]]},"references-count":41,"URL":"https:\/\/doi.org\/10.1109\/etfa45728.2021.9613298","relation":{},"subject":[],"published":{"date-parts":[[2021,9,7]]}}}