用生成模型合成红外缺陷图，提升小样本检测效果
Application Research of a Deep Learning Model Integrating CycleGAN and YOLO in PCB Infrared Defect Detection
- 用CycleGAN将可见光板图转为伪红外图
- 融合生成数据后检测准确率接近全监督模型
- 适合工业质检中红外数据稀缺的场景
针对印刷电路板（PCB）红外缺陷检测中红外数据稀缺的难题，本文提出一种融合CycleGAN与YOLOv8的跨模态数据增强框架。不同于依赖成对标注的传统方法，利用CycleGAN实现无配对图像到图像的转换，将大量可见光PCB图像映射至红外域，生成高保真伪红外样本，保留缺陷结构语义并准确模拟热分布模式。随后采用异构训练策略，将生成的伪红外数据与有限真实红外样本结合，训练轻量级YOLOv8检测器。实验表明，该方法在低数据条件下显著增强特征学习能力，增强后的检测器性能远超仅用真实数据训练的模型，逼近全监督训练表现，验证了伪红外合成作为工业检测中稳健数据增强策略的有效性。
原文摘要 · Abstract (English)
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.
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