用GAN生成缺陷图提升YOLOv11检测精度，专治PCB常见六类缺陷
Defect Detection Network In PCB Circuit Devices Based on GAN Enhanced YOLOv11
- 用GAN合成真实缺陷图像，扩充训练数据
- 在复杂环境中小目标缺陷检测准确率显著提升
- 适合工业界自动化质检，尤其对罕见缺陷有效
本研究提出一种基于GAN增强的YOLOv11模型，用于印刷电路板（PCB）表面缺陷检测。针对缺失孔、鼠咬、开路、短路、毛刺和虚焊六类常见缺陷，利用GAN生成多样化且逼真的合成缺陷图像，扩充数据集，提升模型对复杂及稀有缺陷（如毛刺）的泛化能力。在PCB缺陷数据集上的评估显示，该方法在准确率、召回率和鲁棒性方面均有显著提升，尤其在复杂环境与小目标检测中表现优异。研究推动了电子设计自动化（EDA）领域缺陷检测的智能化发展，减少人工依赖，加速设计到生产的流程。结果表明，融合GAN数据增强与优化检测架构可有效提升工业级PCB质检的可靠性与效率。
原文摘要 · Abstract (English)
This study proposes an advanced method for surface defect detection in printed circuit boards (PCBs) using an improved YOLOv11 model enhanced with a generative adversarial network (GAN). The approach focuses on identifying six common defect types: missing hole, rat bite, open circuit, short circuit, burr, and virtual welding. By employing GAN to generate synthetic defect images, the dataset is augmented with diverse and realistic patterns, improving the model's ability to generalize, particularly for complex and infrequent defects like burrs. The enhanced YOLOv11 model is evaluated on a PCB defect dataset, demonstrating significant improvements in accuracy, recall, and robustness, especially when dealing with defects in complex environments or small targets. This research contributes to the broader field of electronic design automation (EDA), where efficient defect detection is a crucial step in ensuring high-quality PCB manufacturing. By integrating advanced deep learning techniques, this approach enhances the automation and precision of defect detection, reducing reliance on manual inspection and accelerating design-to-production workflows. The findings underscore the importance of incorporating GAN-based data augmentation and optimized detection architectures in EDA processes, providing valuable insights for improving reliability and efficiency in PCB defect detection within industrial applications.
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