{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T05:05:38Z","timestamp":1768971938450,"version":"3.49.0"},"reference-count":48,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T00:00:00Z","timestamp":1683763200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Instituto de Telecomunica\u00e7\u00f5es Lisbon, Portugal","award":["UIDB\/50008\/2020"],"award-info":[{"award-number":["UIDB\/50008\/2020"]}]},{"name":"Instituto de Telecomunica\u00e7\u00f5es Lisbon, Portugal","award":["ISTA-BM-PDCTI-2017"],"award-info":[{"award-number":["ISTA-BM-PDCTI-2017"]}]},{"DOI":"10.13039\/100017159","name":"Iscte\u2014Instituto Universit\u00e1rio de Lisboa","doi-asserted-by":"publisher","award":["UIDB\/50008\/2020"],"award-info":[{"award-number":["UIDB\/50008\/2020"]}],"id":[{"id":"10.13039\/100017159","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017159","name":"Iscte\u2014Instituto Universit\u00e1rio de Lisboa","doi-asserted-by":"publisher","award":["ISTA-BM-PDCTI-2017"],"award-info":[{"award-number":["ISTA-BM-PDCTI-2017"]}],"id":[{"id":"10.13039\/100017159","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Industrial inspection is crucial for maintaining quality and safety in industrial processes. Deep learning models have recently demonstrated promising results in such tasks. This paper proposes YOLOX-Ray, an efficient new deep learning architecture tailored for industrial inspection. YOLOX-Ray is based on the You Only Look Once (YOLO) object detection algorithms and integrates the SimAM attention mechanism for improved feature extraction in the Feature Pyramid Network (FPN) and Path Aggregation Network (PAN). Moreover, it also employs the Alpha-IoU cost function for enhanced small-scale object detection. YOLOX-Ray\u2019s performance was assessed in three case studies: hotspot detection, infrastructure crack detection and corrosion detection. The architecture outperforms all other configurations, achieving mAP50 values of 89%, 99.6% and 87.7%, respectively. For the most challenging metric, mAP50:95, the achieved values were 44.7%, 66.1% and 51.8%, respectively. A comparative analysis demonstrated the importance of combining the SimAM attention mechanism with Alpha-IoU loss function for optimal performance. In conclusion, YOLOX-Ray\u2019s ability to detect and to locate multi-scale objects in industrial environments presents new opportunities for effective, efficient and sustainable inspection processes across various industries, revolutionizing the field of industrial inspections.<\/jats:p>","DOI":"10.3390\/s23104681","type":"journal-article","created":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T01:30:29Z","timestamp":1683855029000},"page":"4681","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["YOLOX-Ray: An Efficient Attention-Based Single-Staged Object Detector Tailored for Industrial Inspections"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5921-0286","authenticated-orcid":false,"given":"Ant\u00f3nio","family":"Raimundo","sequence":"first","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es (IT), Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal"},{"name":"Department of Information Science and Technology, Iscte\u2014Instituto Universit\u00e1rio de Lisboa, Av. das For\u00e7as Armadas, 1649-026 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4759-4817","authenticated-orcid":false,"given":"Jo\u00e3o Pedro","family":"Pavia","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es (IT), Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal"},{"name":"COPELABS, Universidade Lus\u00f3fona, Campo Grande 376, 1749-024 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7729-4033","authenticated-orcid":false,"given":"Pedro","family":"Sebasti\u00e3o","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es (IT), Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal"},{"name":"Department of Information Science and Technology, Iscte\u2014Instituto Universit\u00e1rio de Lisboa, Av. das For\u00e7as Armadas, 1649-026 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5055-6347","authenticated-orcid":false,"given":"Octavian","family":"Postolache","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es (IT), Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal"},{"name":"Department of Information Science and Technology, Iscte\u2014Instituto Universit\u00e1rio de Lisboa, Av. das For\u00e7as Armadas, 1649-026 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1109\/TIE.1930.896476","article-title":"Computer-Vision-Based Fabric Defect Detection: A Survey","volume":"55","author":"Kumar","year":"2008","journal-title":"IEEE Trans. Ind. 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