{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:13:53Z","timestamp":1771697633798,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:00:00Z","timestamp":1704844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"M-Era.Net","award":["2\/0135\/23"],"award-info":[{"award-number":["2\/0135\/23"]}]},{"name":"M-Era.Net","award":["2\/0099\/22"],"award-info":[{"award-number":["2\/0099\/22"]}]},{"name":"M-Era.Net","award":["APVV-20-0042"],"award-info":[{"award-number":["APVV-20-0042"]}]},{"DOI":"10.13039\/501100006109","name":"VEGA","doi-asserted-by":"publisher","award":["2\/0135\/23"],"award-info":[{"award-number":["2\/0135\/23"]}],"id":[{"id":"10.13039\/501100006109","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006109","name":"VEGA","doi-asserted-by":"publisher","award":["2\/0099\/22"],"award-info":[{"award-number":["2\/0099\/22"]}],"id":[{"id":"10.13039\/501100006109","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006109","name":"VEGA","doi-asserted-by":"publisher","award":["APVV-20-0042"],"award-info":[{"award-number":["APVV-20-0042"]}],"id":[{"id":"10.13039\/501100006109","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes a new approach to defect detection system design focused on exact damaged areas demonstrated through visual data containing gear wheel images. The main advantage of the system is the capability to detect a wide range of patterns of defects occurring in datasets. The methodology is built on three processes that combine different approaches from unsupervised and supervised methods. The first step is a search for anomalies, which is performed by defining the correct areas on the controlled object by using the autoencoder approach. As a result, the differences between the original and autoencoder-generated images are obtained. These are divided into clusters using the clustering method (DBSCAN). Based on the clusters, the regions of interest are subsequently defined and classified using the pre-trained Xception network classifier. The main result is a system capable of focusing on exact defect areas using the sequence of unsupervised learning (autoencoder)\u2013unsupervised learning (clustering)\u2013supervised learning (classification) methods (U2S-CNN). The outcome with tested samples was 177 detected regions and 205 occurring damaged areas. There were 108 regions detected correctly, and 69 regions were labeled incorrectly. This paper describes a proof of concept for defect detection by highlighting exact defect areas. It can be thus an alternative to using detectors such as YOLO methods, reconstructors, autoencoders, transformers, etc.<\/jats:p>","DOI":"10.3390\/s24020429","type":"journal-article","created":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T07:50:48Z","timestamp":1704873048000},"page":"429","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["From Anomaly Detection to Defect Classification"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7154-6313","authenticated-orcid":false,"given":"Jarom\u00edr","family":"Klar\u00e1k","sequence":"first","affiliation":[{"name":"Institute of Informatics, Slovak Academy of Sciences, 845 07 Bratislava, Slovakia"}]},{"given":"Robert","family":"Andok","sequence":"additional","affiliation":[{"name":"Institute of Informatics, Slovak Academy of Sciences, 845 07 Bratislava, Slovakia"}]},{"given":"Peter","family":"Mal\u00edk","sequence":"additional","affiliation":[{"name":"Institute of Informatics, Slovak Academy of Sciences, 845 07 Bratislava, Slovakia"}]},{"given":"Ivan","family":"Kuric","sequence":"additional","affiliation":[{"name":"Department of Automation and Production Systems, Faculty of Mechanical Engineering, University of Zilina, 010 26 Zilina, Slovakia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7922-408X","authenticated-orcid":false,"given":"M\u00e1rio","family":"Ritomsk\u00fd","sequence":"additional","affiliation":[{"name":"Institute of Informatics, Slovak Academy of Sciences, 845 07 Bratislava, Slovakia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3291-2963","authenticated-orcid":false,"given":"Ivana","family":"Kla\u010dkov\u00e1","sequence":"additional","affiliation":[{"name":"Department of Automation and Production Systems, Faculty of Mechanical Engineering, University of Zilina, 010 26 Zilina, Slovakia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0940-7531","authenticated-orcid":false,"given":"Hung-Yin","family":"Tsai","sequence":"additional","affiliation":[{"name":"Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"150","DOI":"10.2478\/msr-2020-0018","article-title":"Novel method of contactless sensing of mechanical quantities","volume":"20","author":"Mierka","year":"2020","journal-title":"Meas. 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