深度学习模型整合CycleGAN和YOLO在PCB红外缺陷检测中的应用研究
Application Research of a Deep Learning Model Integrating CycleGAN and YOLO in PCB Infrared Defect Detection
AI总结：
本文提出整合CycleGAN和YOLOv8的深度学习模型，通过生成伪红外样本提升PCB缺陷检测在低数据条件下的性能。
摘要：
本文针对印刷电路板（PCB）缺陷检测中红外数据稀缺的关键瓶颈，提出了一种整合CycleGAN和YOLOv8的跨模态数据增强框架。不同于传统方法依赖配对监督，我们利用CycleGAN进行无配对图像到图像翻译，将丰富的可见光PCB图像映射到红外域。生成过程合成高保真的伪红外样本，保留缺陷的结构语义同时准确模拟热分布模式。随后，我们构建了融合生成的伪红外数据与有限真实红外样本的异构训练策略，以训练轻量级的YOLOv8检测器。实验结果表明，该方法在低数据条件下有效提升了特征学习。增强的检测器显著优于仅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 robust 增强策略的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
