Photovoltaics Cell Anomaly Detection Using Deep Learning Techniques

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dc.contributor.authorAl-Dulaimi, A.A.
dc.contributor.authorHameed, A.A.
dc.contributor.authorGuneser, M.T.
dc.contributor.authorJamil, A.
dc.date.accessioned2024-05-19T14:33:49Z
dc.date.available2024-05-19T14:33:49Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description2nd International Conference on Advanced Engineering, Technology and Applications, ICAETA 2023 -- 10 March 2023 through 11 March 2023 -- -- 305999en_US
dc.description.abstractPhotovoltaic cells play a crucial role in converting sunlight into electrical energy. However, defects can occur during the manufacturing process, negatively impacting these cells’ efficiency and overall performance. Electroluminescence (EL) imaging has emerged as a viable method for defect detection in photovoltaic cells. Developing an accurate and automated detection model capable of identifying and classifying defects in EL images holds significant importance in photovoltaics. This paper introduces a state-of-the-art defect detection model based on the Yolo v.7 architecture designed explicitly for photovoltaic cell electroluminescence images. The model is trained to recognize and categorize five common defect classes, namely black core (Bc), crack (Ck), finger (Fr), star crack (Sc), and thick line (Tl). The proposed model exhibits remarkable performance through experimentation with an average precision of 80%, recall of 87%, and an mAP@.5 score of 86% across all defect classes. Furthermore, a comparative analysis is conducted to evaluate the model’s performance against two recently proposed models. The results affirm the excellent performance of the proposed model, highlighting its superiority in defect detection within the context of photovoltaic cell electroluminescence images. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.identifier.doi10.1007/978-3-031-50920-9_13
dc.identifier.endpage174en_US
dc.identifier.isbn9783031509193
dc.identifier.issn1865-0929
dc.identifier.scopus2-s2.0-85180789387en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage159en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-50920-9_13
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4344
dc.identifier.volume1983 CCISen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofCommunications in Computer and Information Scienceen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectDeep Learningen_US
dc.subjectDetectionen_US
dc.subjectElectroluminescence İmage Detectionen_US
dc.subjectSolar Panelen_US
dc.titlePhotovoltaics Cell Anomaly Detection Using Deep Learning Techniquesen_US
dc.typeConference Objecten_US

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