Sung Ho Park  Seung Hoon Lee

10.5626/JOK.2025.52.1.70

Defect Detection Deep Image Comparison Contrastive Attention Contrastive Loss Function

Effective classification of defects in Printed Circuit Boards (PCBs) is critical for ensuring product quality. Traditional approaches to PCB defect detection have primarily relied on single-image analysis or failed to adequately address alignment issues between reference and test images, leading to reduced reliability and precision in defect detection. To overcome these limitations, this study aimed to introduce a novel deep image comparison method that could incorporate contrastive loss functions to improve image alignment with a contrastive attention mechanism to focus the model on areas with a higher likelihood of defects. Experiments conducted on actual PCB data demonstrated that the proposed method achieved superior classification performance, even with limited data, highlighting its potential to significantly enhance the reliability of PCB defect detection and address existing challenges in the field.