Automated Defect Detection for Mass-Produced Electronic Components Based on YOLO Object Detection Models

Since the defect detection of conventional industry components is time-consuming and labor-intensive, it leads to a significant burden on quality inspection personnel and difficult to manage product quality. In this article, we propose an automated defect detection system for the dual in-line package (DIP) that is widely used in industry, using digital camera optics and deep learning (DL)-based model. The two most common defect categories of DIP are examined: <inline-formula> <tex-math notation="LaTeX">$\text {1)}$ </tex-math></inline-formula> surface defects and <inline-formula> <tex-math notation="LaTeX">$\text {2)}$ </tex-math></inline-formula> pin-leg defects. However, the lack of defective component images leads to a challenge for detection tasks. To solve this problem, the ConSinGAN is used to generate a suitable size dataset for training and testing. Four varieties of the you only look once (YOLO) model are investigated (v3, v4, v7, and v9), both in isolation and with the ConSinGAN augmentation. The proposed YOLOv7 with ConSinGAN is superior to the other YOLO versions in accuracy of 95.50%, detection time of 285 ms, and is far superior to threshold-based approaches. In addition, the supervisory control and data acquisition (SCADA) system is developed, and the associated sensor architecture is described. The proposed automated defect detection can be easily established with numerous types of defects or insufficient defect data.