Object detection model-based quality inspection using a deep CNN

In the competitive world of the metal industry where companies have to offer quality products, quality control is crucial. However, it takes a considerable amount of time, especially if it is a manual process. Automatic Fault Detection (AFD) system reduces a lot of work for the companies, saves time, money and improves use of available resources. Deep learning can be efficiently used to develop such a AFD system. In this article, we present the development of deep learning (DL) algorithms for quality control. We trained State-of the-art DL (YOLO v8n, YOLO v8s, YOLO v8m, YOLO v8l and YOLO v8x) for a quality control task using a manually annotated dataset of 3 classes (neck scratch, scratch and bent) for 2 objects (Screw and Metal Nut). The results show very interesting scores for YOLO v8s with an mAP@0.50 of 90.60%, a precision of 100% and a recall of 94.0% for the 3 classes on average. We also compared the performance of these models with a popular DL model detector called Faster-RCNN x101 in order to confirm the performance of the developed models. The qualitative results show good detection of defects with different sizes (small, medium and large). Our proposition gives very interesting results to deploy an AFD system for metal industries.