{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T17:56:29Z","timestamp":1783014989804,"version":"3.54.6"},"reference-count":71,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T00:00:00Z","timestamp":1656288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada (NSERC)","doi-asserted-by":"publisher","award":["DDG-2020-00034"],"award-info":[{"award-number":["DDG-2020-00034"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Power distribution grids are typically installed outdoors and are exposed to environmental conditions. When contamination accumulates in the structures of the network, there may be shutdowns caused by electrical arcs. To improve the reliability of the network, visual inspections of the electrical power system can be carried out; these inspections can be automated using computer vision techniques based on deep neural networks. Based on this need, this paper proposes the Semi-ProtoPNet deep learning model to classify defective structures in the power distribution networks. The Semi-ProtoPNet deep neural network does not perform convex optimization of its last dense layer to maintain the impact of the negative reasoning process on image classification. The negative reasoning process rejects the incorrect classes of an input image; for this reason, it is possible to carry out an analysis with a low number of images that have different backgrounds, which is one of the challenges of this type of analysis. Semi-ProtoPNet achieves an accuracy of 97.22%, being superior to VGG-13, VGG-16, VGG-19, ResNet-34, ResNet-50, ResNet-152, DenseNet-121, DenseNet-161, DenseNet-201, and also models of the same class such as ProtoPNet, NP-ProtoPNet, Gen-ProtoPNet, and Ps-ProtoPNet.<\/jats:p>","DOI":"10.3390\/s22134859","type":"journal-article","created":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T00:07:02Z","timestamp":1656374822000},"page":"4859","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Semi-ProtoPNet Deep Neural Network for the Classification of Defective Power Grid Distribution Structures"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3723-616X","authenticated-orcid":false,"given":"Stefano Frizzo","family":"Stefenon","sequence":"first","affiliation":[{"name":"Fondazione Bruno Kessler, Via Sommarive 18, 38123 Trento, Italy"},{"name":"Department of Mathematics, Informatics and Physical Sciences, University of Udine, Via delle Scienze 206, 33100 Udine, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0819-8221","authenticated-orcid":false,"given":"Gurmail","family":"Singh","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Applied Science, University of Regina, Wascana Parkway 3737, Regina, SK S4S 0A2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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, Wascana Parkway 3737, Regina, SK S4S 0A2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1315-6990","authenticated-orcid":false,"given":"Alessandro","family":"Cimatti","sequence":"additional","affiliation":[{"name":"Fondazione Bruno Kessler, Via Sommarive 18, 38123 Trento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1049\/hve2.12081","article-title":"Classification of partial discharge severities of ceramic insulators based on texture analysis of UV pulses","volume":"6","author":"Ma","year":"2021","journal-title":"High Volt."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1049\/iet-smt.2020.0083","article-title":"Fault detection in insulators based on ultrasonic signal processing using a hybrid deep learning technique","volume":"14","author":"Stefenon","year":"2020","journal-title":"IET Sci. 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