School of Computer Science, China University of Geosciences, Wuhan 430078, China.
Hubei Key Laboratory of Intelligent Geo-Information Processing, Wuhan 430078, China.
Comput Intell Neurosci. 2021 Oct 12;2021:7550670. doi: 10.1155/2021/7550670. eCollection 2021.
After the production of printed circuit boards (PCB), PCB manufacturers need to remove defected boards by conducting rigorous testing, while manual inspection is time-consuming and laborious. Many PCB factories employ automatic optical inspection (AOI), but this pixel-based comparison method has a high false alarm rate, thus requiring intensive human inspection to determine whether alarms raised from it resemble true or pseudo defects. In this paper, we propose a new cost-sensitive deep learning model: cost-sensitive siamese network (CSS-Net) based on siamese network, transfer learning and threshold moving methods to distinguish between true and pseudo PCB defects as a cost-sensitive classification problem. We use optimization algorithms such as NSGA-II to determine the optimal cost-sensitive threshold. Results show that our model improves true defects prediction accuracy to 97.60%, and it maintains relatively high pseudo defect prediction accuracy, 61.24% in real-production scenario. Furthermore, our model also outperforms its state-of-the-art competitor models in other comprehensive cost-sensitive metrics, with an average of 33.32% shorter training time.
电路板（PCB）生产完成后，制造商需要通过严格的测试来剔除缺陷板，而人工检测既耗时又费力。许多 PCB 工厂采用自动光学检测（AOI），但这种基于像素的比较方法误报率很高，因此需要密集的人工检查来确定它所提出的警报是真正的缺陷还是伪缺陷。在本文中，我们提出了一种新的基于代价敏感孪生网络（CSS-Net）的代价敏感深度学习模型：基于孪生网络、迁移学习和阈值移动方法，将区分真实和伪 PCB 缺陷作为代价敏感分类问题。我们使用 NSGA-II 等优化算法来确定最优的代价敏感阈值。结果表明，我们的模型将真实缺陷的预测准确率提高到了 97.60%，同时在实际生产场景中保持了相对较高的伪缺陷预测准确率，为 61.24%。此外，我们的模型在其他综合代价敏感指标上也优于最先进的竞争模型，平均训练时间缩短了 33.32%。
