Automated Printed Circuit Board Inspection: Incorporating YOLOv8 for Efficient Defect Detection

Abstract

In the realm of modern electronics, Printed Circuit Boards (PCBs) serve as fundamental components, facilitating the seamless integration of circuits across a wide spectrum of applications, from smartphones to space exploration. However, addressing the formidable challenge of efficiently detecting concealed threats within complex environments, where they frequently camouflage themselves within the background, has become a serious threat. To tackle this intricate challenge, the implementation of an Automatic Visual Inspection (AVI) system emerges as an indispensable necessity in today's world. The objective of this research is to create a proficient system for detecting defects in printed circuit boards (PCBs) by employing YOLOv8, the most recent advancement in the YOLO series of object detection models. Our model has achieved a mean Average Precision (mAP50) of 0.955 and (mAP50-95) of 0.516, ensuring the robust detection of PCB defects. Experimental results have revealed that this model exhibits enhanced detection accuracy in comparison to conventional and deep learning object detection models. To assess the performance of this model a comparison with the previous iteration, YOLOv7 with the same experimentation scenarios was conducted. The results demonstrated that the Yolov8 had 17% increment in the mAP50 metric.