English(EN) Application Research of a Deep Learning Model Integrating CycleGAN and YOLO in PCB Infrared Defect Detection

深度学习模型融合CycleGAN和YOLO用于PCB缺陷检测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-05 04:00

本文提出了一种使用红外（IR）图像进行印刷电路板（PCB）缺陷检测的新颖框架，解决了IR数据有限的挑战。该方法采用CycleGAN进行不成对的图像到图像翻译，从可见光图像生成合成IR图像，模拟热模式。这些合成图像与有限的真实IR数据结合，用于训练YOLOv8检测器，显著提高了在数据稀疏场景下的性能。 AI

影响引入了一种利用生成模型进行合成数据增强来克服工业检测中数据稀缺性问题的方法。

排序理由这是一篇研究论文，详细介绍了深度学习模型在缺陷检测中的新颖应用。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Chao Yang, Haoyuan Zheng, Yue Ma · 2026-05-05 04:00

CycleGAN与YOLO融合的深度学习模型在PCB红外缺陷检测中的应用研究

arXiv:2601.00237v2 Announce Type: replace Abstract: 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 conventio…

本文如何被排名

报道来源 [1]

CycleGAN与YOLO融合的深度学习模型在PCB红外缺陷检测中的应用研究

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