A Design of Bare Printed Circuit Board Defect Detection System Based on YOLOv8

Abstract

As electronic products develop towards miniaturization and digitization, printed circuit boards (PCBs) also develop towards high density and high precision. In the manufacturing process of PCBs, some PCBs with defects will be produced, and these defects often lead to circuit failure, so defect detection technology is an indispensable part of PCB manufacturing technology. Aiming at the problems of low efficiency and accuracy of traditional image recognition and classification technology, A PCB defect detection algorithm is proposed based on YOLOv8 in this paper. For these five PCB defects, the neural network in deep learning was used to identify and classify PCB defects. The prediction accuracy of YOLOv8 model after training is close to 97%, and it is compared with the accuracy of other algorithms to prove the effectiveness and feasibility of the model. In addition, the object detection image user interface is also established in this paper, which can realize image detection more conveniently.