Department of Mechanical Engineering, National Taiwan University, Taipei 10617, Taiwan.
Taiwan Instrument Research Institute, National Applied Research Laboratories, Hsinchu 30076, Taiwan.
Sensors (Basel). 2020 Jun 12;20(12):3336. doi: 10.3390/s20123336.
Recently, researchers have been studying methods to introduce deep learning into automated optical inspection (AOI) systems to reduce labor costs. However, the integration of deep learning in the industry may encounter major challenges such as sample imbalance (defective products that only account for a small proportion). Therefore, in this study, an anomaly detection neural network, dual auto-encoder generative adversarial network (DAGAN), was developed to solve the problem of sample imbalance. With skip-connection and dual auto-encoder architecture, the proposed method exhibited excellent image reconstruction ability and training stability. Three datasets, namely public industrial detection training set, MVTec AD, with mobile phone screen glass and wood defect detection datasets, were used to verify the inspection ability of DAGAN. In addition, training with a limited amount of data was proposed to verify its detection ability. The results demonstrated that the areas under the curve (AUCs) of DAGAN were better than previous generative adversarial network-based anomaly detection models in 13 out of 17 categories in these datasets, especially in categories with high variability or noise. The maximum AUC improvement was 0.250 (toothbrush). Moreover, the proposed method exhibited better detection ability than the U-Net auto-encoder, which indicates the function of discriminator in this application. Furthermore, the proposed method had a high level of AUCs when using only a small amount of training data. DAGAN can significantly reduce the time and cost of collecting and labeling data when it is applied to industrial detection.
最近，研究人员一直在研究将深度学习引入自动化光学检测（AOI）系统的方法，以降低人工成本。然而，深度学习在工业中的集成可能会遇到重大挑战，例如样本不平衡（缺陷产品仅占很小的比例）。因此，在这项研究中，开发了一种异常检测神经网络，即双自动编码器生成对抗网络（DAGAN），以解决样本不平衡的问题。该方法采用跳跃连接和双自动编码器结构，具有出色的图像重建能力和训练稳定性。使用了三个数据集，即公共工业检测训练集、具有手机屏幕玻璃和木材缺陷检测数据集的 MVTec AD，来验证 DAGAN 的检测能力。此外，还提出了使用有限数量的数据进行训练，以验证其检测能力。结果表明，在这些数据集中的 17 个类别中的 13 个类别中，DAGAN 的曲线下面积（AUC）优于之前基于生成对抗网络的异常检测模型，特别是在具有高可变性或噪声的类别中。最大 AUC 提高了 0.250（牙刷）。此外，与 U-Net 自动编码器相比，该方法表现出更好的检测能力，这表明判别器在该应用中的作用。此外，当仅使用少量训练数据时，该方法具有较高的 AUC。DAGAN 在应用于工业检测时，可以显著减少数据收集和标记的时间和成本。
