Application Research of a Deep Learning Model Integrating CycleGAN and YOLO in PCB Infrared Defect Detection
This addresses a critical bottleneck in industrial inspection for PCB manufacturing by enabling effective defect detection with limited infrared data, though it is incremental as it combines existing methods.
This paper tackles the problem of infrared data scarcity in PCB defect detection by using CycleGAN to generate pseudo-infrared images from visible-light data and training a YOLOv8 detector with this augmented dataset, resulting in a detector that significantly outperforms models trained on limited real data and approaches fully supervised benchmarks.
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. This generative process synthesizes high-fidelity pseudo-IR samples that preserve the structural semantics of defects while accurately simulating thermal distribution patterns. Subsequently, we construct a heterogeneous training strategy that fuses generated pseudo-IR data with limited real IR samples to train a lightweight YOLOv8 detector. Experimental results demonstrate that this method effectively enhances feature learning under low-data conditions. The augmented detector significantly outperforms models trained on limited real data alone and approaches the performance benchmarks of fully supervised training, proving the efficacy of pseudo-IR synthesis as a robust augmentation strategy for industrial inspection.