AI RESEARCH
Application Research of a Deep Learning Model Integrating CycleGAN and YOLO in PCB Infrared Defect Detection
arXiv CS.LG
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ArXi:2601.00237v2 Announce Type: replace-cross This paper addresses the critical bottleneck of infrared (IR) data scarcity in Printed Circuit Board (PCB) defect detection by proposing a cross-modal data augmentation framework integrating CycleGAN and YOLOv8. Unlike conventional methods relying on paired supervision, we leverage CycleGAN to perform unpaired image-to-image translation, mapping abundant visible-light PCB images into the infrared domain.