TY  - JOUR
T1  - Reference Image-Based Contrastive Attention Mechanism for Printed Circuit Board Defect Classification
AU  - Park, Sung Ho 
AU  - Lee, Seung Hoon 
JO  - Journal of KIISE, JOK
PY  - 2025
DA  - 2025/1/14
DO  - 10.5626/JOK.2025.52.1.70
KW  - Defect Detection
KW  - Deep Image Comparison
KW  - Contrastive Attention
KW  - Contrastive Loss Function
AB  - 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.