The integration of AI into automated optical inspection enables the enhancement of printed circuit board manufacturing efficiency, accuracy, and cost-effectiveness. We developed an AI-assisted, 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.