{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T14:53:45Z","timestamp":1776783225801,"version":"3.51.2"},"reference-count":78,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T00:00:00Z","timestamp":1631145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT - VALORIZA \u2013 Research Centre for Endogenous Resource Valorization)","award":["UIDB\/05064\/2020"],"award-info":[{"award-number":["UIDB\/05064\/2020"]}]},{"name":"COPELABS (COFAC\/ILIND\/COPELABS 2020)","award":["UIDB\/04111\/2020"],"award-info":[{"award-number":["UIDB\/04111\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Contamination on insulators may increase the surface conductivity of the insulator, and as a consequence, electrical discharges occur more frequently, which can lead to interruptions in a power supply. To maintain reliability in an electrical distribution power system, components that have lost their insulating properties must be replaced. Identifying the components that need maintenance is a difficult task as there are several levels of contamination that are hard to notice during inspections. To improve the quality of inspections, this paper proposes using k-nearest neighbors (k-NN) to classify the levels of insulator contamination based on images of insulators at various levels of contamination simulated in the laboratory. Computer vision features such as mean, variance, asymmetry, kurtosis, energy, and entropy are used for training the k-NN. To assess the robustness of the proposed approach, a statistical analysis and a comparative assessment with well-consolidated algorithms such as decision tree, ensemble subspace, and support vector machine models are presented. The k-NN showed up to 85.17% accuracy using the k-fold cross-validation method, with an average accuracy higher than 82% for the multi-classification of contamination of insulators, being superior to the compared models.<\/jats:p>","DOI":"10.3390\/computers10090112","type":"journal-article","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T12:20:37Z","timestamp":1631190037000},"page":"112","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8475-1201","authenticated-orcid":false,"given":"Marcelo Picolotto","family":"Corso","sequence":"first","affiliation":[{"name":"Electrical Engineering Graduate Program, Regional University of Blumenau, R. S\u00e3o Paulo 3250 (Itoupava Seca), Blumenau 89030-000, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5223-1562","authenticated-orcid":false,"given":"Fabio Luis","family":"Perez","sequence":"additional","affiliation":[{"name":"Electrical Engineering Graduate Program, Regional University of Blumenau, R. S\u00e3o Paulo 3250 (Itoupava Seca), Blumenau 89030-000, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3723-616X","authenticated-orcid":false,"given":"St\u00e9fano Frizzo","family":"Stefenon","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8610-661X","authenticated-orcid":false,"given":"Kin-Choong","family":"Yow","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2862-3053","authenticated-orcid":false,"given":"Ra\u00fal","family":"Garc\u00eda Ovejero","sequence":"additional","affiliation":[{"name":"Expert Systems and Applications Lab., E.T.S.I.I. of B\u00e9jar, University of Salamanca, 37008 Salamanca, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0446-9271","authenticated-orcid":false,"given":"Valderi Reis Quietinho","family":"Leithardt","sequence":"additional","affiliation":[{"name":"VALORIZA, Research Center for Endogenous Resources Valorization, Instituto Polit\u00e9cnico de Portalegre, 7300-555 Portalegre, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6655","DOI":"10.3233\/JIFS-190013","article-title":"Fault diagnosis of insulators from ultrasound detection using neural networks","volume":"37","author":"Stefenon","year":"2019","journal-title":"J. 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