{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T16:04:29Z","timestamp":1762445069165,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T00:00:00Z","timestamp":1602460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["001"],"award-info":[{"award-number":["001"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["306850\/2016-8"],"award-info":[{"award-number":["306850\/2016-8"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Conveyor belts are the most widespread means of transportation for large quantities of materials in the mining sector. Therefore, autonomous methods that can help human beings to perform the inspection of the belt conveyor system is a major concern for companies. In this context, we present in this work a novel and automatic visual detector that recognizes dirt buildup on the structures of conveyor belts, which is one of the tasks of the maintenance inspectors. This visual detector can be embedded as sensors in autonomous robots for the inspection activity. The proposed system involves training a convolutional neural network from RGB images. The use of the transfer learning technique, i.e., retraining consolidated networks for image classification with our collected images has shown very effective. Two different approaches for transfer learning have been analyzed. The best one presented an average accuracy of 0.8975 with an F-1 Score of 0.8773 for the dirt recognition. A field validation experiment served to evaluate the performance of the proposed system in a real time classification task.<\/jats:p>","DOI":"10.3390\/s20205762","type":"journal-article","created":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T21:24:39Z","timestamp":1602710679000},"page":"5762","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Automatic System for Visual Detection of Dirt Buildup on Conveyor Belts Using Convolutional Neural Networks"],"prefix":"10.3390","volume":"20","author":[{"given":"Andr\u00e9 A.","family":"Santos","sequence":"first","affiliation":[{"name":"Programa de P\u00f3s-Gradua\u00e7\u00e3o em Instrumenta\u00e7\u00e3o, Controle e Automa\u00e7\u00e3o de Processos de Minera\u00e7\u00e3o, Universidade Federal de Ouro Preto e Instituto Tecnol\u00f3gico Vale, Minas Gerais 35400-000, Brazil"},{"name":"Robotics Lab, Vale Institute of Technology (ITV), Minas Gerais 35400-000, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2167-1973","authenticated-orcid":false,"given":"Filipe A. S.","family":"Rocha","sequence":"additional","affiliation":[{"name":"Robotics Lab, Vale Institute of Technology (ITV), Minas Gerais 35400-000, Brazil"}]},{"given":"Agnaldo J. da R.","family":"Reis","sequence":"additional","affiliation":[{"name":"Department of Control Engineering and Automation, School of Mines, Federal University of Ouro Preto (UFOP), Minas Gerais 35000-400, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9238-8839","authenticated-orcid":false,"given":"Frederico G.","family":"Guimar\u00e3es","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Federal University of Minas Gerais (UFMG), Minas Gerais 31270-901, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,12]]},"reference":[{"key":"ref_1","unstructured":"(2007). Conveyor Equipment Manufactures Association. Belt Conveyors for Bulk Materials, k-kom. 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