{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:47:23Z","timestamp":1778604443212,"version":"3.51.4"},"reference-count":30,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T00:00:00Z","timestamp":1663027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Pallet racking is an essential element within warehouses, distribution centers, and manufacturing facilities. To guarantee its safe operation as well as stock protection and personnel safety, pallet racking requires continuous inspections and timely maintenance in the case of damage being discovered. Conventionally, a rack inspection is a manual quality inspection process completed by certified inspectors. The manual process results in operational down-time as well as inspection and certification costs and undiscovered damage due to human error. Inspired by the trend toward smart industrial operations, we present a computer vision-based autonomous rack inspection framework centered around YOLOv7 architecture. Additionally, we propose a domain variance modeling mechanism for addressing the issue of data scarcity through the generation of representative data samples. Our proposed framework achieved a mean average precision of 91.1%.<\/jats:p>","DOI":"10.3390\/s22186927","type":"journal-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T22:37:28Z","timestamp":1663108648000},"page":"6927","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":71,"title":["Domain Feature Mapping with YOLOv7 for Automated Edge-Based Pallet Racking Inspections"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8458-6202","authenticated-orcid":false,"given":"Muhammad","family":"Hussain","sequence":"first","affiliation":[{"name":"Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1920-7418","authenticated-orcid":false,"given":"Hussain","family":"Al-Aqrabi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2392-6792","authenticated-orcid":false,"given":"Muhammad","family":"Munawar","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University of Islamabad, Islamabad 45550, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0105-7730","authenticated-orcid":false,"given":"Richard","family":"Hill","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK"}]},{"given":"Tariq","family":"Alsboui","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Farahnakian, F., Koivunen, L., Makila, T., and Heikkonen, J. 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