Real-time detection of plastic part surface defects using deep learning-based object detection model

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dc.authorid0000-0002-0298-2170
dc.authorid0000-0001-6023-2901
dc.authorid0000-0002-0729-633X
dc.contributor.authorCelik, Mirac Tuba
dc.contributor.authorArslankaya, Seher
dc.contributor.authorYildiz, Aytac
dc.date.accessioned2026-02-08T15:15:22Z
dc.date.available2026-02-08T15:15:22Z
dc.date.issued2024
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractIn this study, it was aimed to detect defects in plastic parts produced in a company operating in the automotive sub -industry using the YOLOv8 object detection model. The defect types seen in plastic parts were evaluated with the help of Pareto analysis, and scratches, stains and shine were selected as the most common defect types, and data on the three defect types were collected. YOLOv8 models were trained using faulty part images. As a result of the training, the highest mean average precision value of 0.990 was obtained in the YOLOv8s model, and the shortest training time was obtained in the YOLOv8n model. In the YOLOv8s model, which gave the highest mAP value, hyperparameter adjustment was made according to the batch size and learning rate values. The testing phase was carried out with the hyperparameter values that gave the best results and the mAP value was obtained as 0.902.
dc.identifier.doi10.1016/j.measurement.2024.114975
dc.identifier.issn0263-2241
dc.identifier.issn1873-412X
dc.identifier.scopus2-s2.0-85194278180
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.measurement.2024.114975
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5749
dc.identifier.volume235
dc.identifier.wosWOS:001248084900002
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofMeasurement
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectDeep learning
dc.subjectQuality control
dc.subjectDefect detection
dc.subjectArtificial intelligence
dc.subjectYou-Only-Look-Once version 8
dc.titleReal-time detection of plastic part surface defects using deep learning-based object detection model
dc.typeArticle

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