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• W3086867352 abstract "The automotive industry has an extremely high-quality product standard, not just for the security risks each faulty component can present, but the very brand image it must uphold at all times to stay competitive. In this paper, a prototype model is proposed for smart quality inspection using machine vision. The engine labels are detected using Faster-RCNN and YOLOv3 object detection algorithms. All the experiments were carried out using a custom dataset collected at an automotive assembly plant. Eight engine labels of two brands (Citroen and Peugeot) and more than ten models were detected. The results were evaluated using the metrics Intersection of Union (IoU), mean of Average Precision (mAP), Confusion Matrix, Precision and Recall. The results were validated in three folds. The models were trained using a custom dataset containing images and annotation files collected and prepared manually. Data Augmentation techniques were applied to increase the image diversity. The result without data augmentation was 92.5%, and with it the value was up-to 100%. Faster-RCNN has more accurate results compared to YOLOv3." @default.
• W3086867352 created "2020-09-21" @default.
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• W3086867352 date "2020-09-09" @default.
• W3086867352 modified "2023-10-02" @default.
• W3086867352 title "Engine Labels Detection for Vehicle Quality Verification in the Assembly Line: A Machine Vision Approach" @default.
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• W3086867352 doi "https://doi.org/10.1007/978-3-030-58653-9_71" @default.
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