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.

翻译：本文针对印刷电路板（PCB）缺陷检测中红外（IR）数据稀缺这一关键瓶颈，提出了一种集成CycleGAN与YOLOv8的跨模态数据增强框架。不同于依赖配对监督的传统方法，我们利用CycleGAN进行非配对图像到图像的转换，将丰富的可见光PCB图像映射到红外域。这一生成过程合成了高保真度的伪红外样本，这些样本在准确模拟热分布模式的同时，保留了缺陷的结构语义。随后，我们构建了一种异构训练策略，将生成的伪红外数据与有限的实际红外样本相融合，以训练一个轻量级的YOLOv8检测器。实验结果表明，该方法在低数据条件下有效增强了特征学习。经过增强的检测器性能显著优于仅使用有限真实数据训练的模型，并接近全监督训练的性能基准，证明了伪红外合成作为工业检测中一种鲁棒增强策略的有效性。