PCB Board Defect Detection Method based on Improved YOLOv8

Abstract

This study provides an improved YOLOv8-based printed circuit board (PCB) defect identification method to address the current challenges associated with PCB defect detection, including the detection of small targets, low accuracy, and other related concerns. The YOLOv8 model serves as the foundational framework, and in order to enhance detection speed, the YOLOv8s model is selected due to its reduced parameter count. However, feature extraction becomes challenging for small target defects; to address this, the CA attention mechanism is implemented, which is more attuned to target feature information and aids in feature extraction. As indicated by the experimental findings, the enhanced YOLOv8s-CA algorithm model has the following characteristics: a footprint of 5.79 MB, a mean average precision (mAP) of 90.4 percent, an increase of 6.6 percent over the initial network, and a parameter count augmentation of merely 0.007M. Consequently, this model finds utility in compact industrial inspection apparatus and possesses a wide range of potential applications.