YOLOv8-based PCB Defect Detection and Classification System

Surface inspection of Printed Circuit Boards (PCB) is one of the most crucial quality control processes due to potential serious costs of even small errors occurred during production. In this study, a YOLOv8 based system is developed for detection and classification of six common errors occurs on PCBs. In terms of accuracy, speed, and the ability to detect multiple defects simultaneously, proposed method is more suitable for use in production compared to other PCB defect detection methods. Proposed system also offers customizable defect selection for targeted inspection. Experimental results show an impressive mean average precision of 99.2%. Combination of high accuracy, fast processing speed, stability, and user-friendly interface makes it a promising candidate for industrial applications demonstrate the system's suitability for real-world PCB manufacturing environments.