AI-assisted Vision-based Sensing System for Printed Circuit Board Defect Detection Using You Only Look Once

The integration of AI into automated optical inspection enables the enhancement of printed circuit board manufacturing efficiency, accuracy, and cost-effectiveness.We developed an AIassisted, vision-based defect sensing system that conducts multiclass electronic component recognition and surface defect detection by coupling an industrial optical sensing module with an enhanced You Only Look Once version 9-e (YOLOv9-e) deep learning architecture.The developed system enables the integration of controlled ring lighting and a high-resolution CMOS visual sensor to overcome image degradation in raw sensory signals, establishing a highly accurate, noncontact optical inspection concept.Printed circuit board samples containing diverse soldering defects from a national technical examination framework were utilized to compile a dataset of 220 images.Comparative YOLOv9-e outperformed YOLOv7, achieving a component recognition mean average precision at the intersection over union threshold of 0.50 (mAP@0.5) of 93.6%, an F1-score of 90.0%, and a defect detection accuracy of 89.8%.Although the noncontact sensing configuration of the developed system provides robust, real-time diagnostic capabilities, limitations exist, including dataset diversity and susceptibility to ambient illumination variations during sub-millimeter solder wetting inspection.To address the limitations, computational structural re-parameterization in the network layers is required to preserve critical geometric reflections.