{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T04:28:17Z","timestamp":1729225697278,"version":"3.27.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>Industrial anomaly detection models proved to be a viable solution for automating the task of visual inspection. Despite their effectiveness, the deployment of current deep learning models for anomaly detection for high-resolution images involves large memory requirements, which are unattainable in industrial deployments on edge devices. In this paper we introduce a tiling approach for a memory bank based feature embedding model to reduce the memory requirements during training and inference. Additionally, we analyze the correlations between image size, dataset size and required memory. To evaluate the detection performance of different tiling schemes, we create and apply a real industrial training and validation dataset collected in the electric motor housing production of the BMW Group. Our experiments show that depending on the tiling scheme the overall required memory can be decreased by a factor of 12 while leaving the detection performance unchanged and even increase the detection performance when the memory requirement is only halved. This venture underscores the viability of our tiling approach in real-world industrial applications but also marks a significant leap towards resolving the issue of resolution limitations in deploying advanced anomaly detection models.<\/jats:p>","DOI":"10.3233\/faia240704","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:09:41Z","timestamp":1729170581000},"source":"Crossref","is-referenced-by-count":0,"title":["Memory Adaptive and Spatially Specialized Model Ensembles for Industrial Anomaly Detection"],"prefix":"10.3233","author":[{"given":"Marco","family":"Wagenstetter","sequence":"first","affiliation":[{"name":"Powertrain production system planning, BMW Group, Germany"}]},{"given":"Niklas","family":"Landerer","sequence":"additional","affiliation":[{"name":"School of computation, information and technology, Technical University of Munich, Germany"}]},{"given":"Johannes","family":"Thyroff","sequence":"additional","affiliation":[{"name":"School of computation, information and technology, Technical University of Munich, Germany"}]},{"given":"Thomas","family":"Aicher","sequence":"additional","affiliation":[{"name":"Powertrain production system planning, BMW Group, Germany"}]},{"given":"Arvid","family":"Hellmich","sequence":"additional","affiliation":[{"name":"Institute for machine tools and forming technology, Fraunhofer IWU, Germany"}]},{"given":"Steffen","family":"Ihlenfeldt","sequence":"additional","affiliation":[{"name":"Institute for machine tools and forming technology, Fraunhofer IWU, Germany"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240704","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:09:42Z","timestamp":1729170582000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240704"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240704","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}