AI-assisted Vision-based Sensing System for Printed Circuit Board Defect Detection Using You Only Look Once
Year: 2026
Type: article
Abstract: 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 ([email protected]) 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.
Source: Sensors and Materials
DOI: 10.18494/sam5912
Authors: Yi-Heng Wu, Chung-Wen Hung, Yi-An Chen, Lung-Fa Wu
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $748
OpenAlex ID: W7166556745
FWCI:
Cites:
Cited by:
Related to: