In this paper, the unsupervised autoencoder learning for automated defect detection in manufacturing is evaluated, where only the defect-free samples are required for the model training. The loss function of a Convolutional Autoencoder (CAE) model only aims at minimizing the reconstruction errors, and makes the representative features widely spread. The proposed CAE in this study incorporates a regularization that improves the feature distribution of defect-free samples within a tight range. It makes the representative feature vectors of all training samples as close as possible to the mean feature vector so that a defect sample in the evaluation stage can generate a distinct distance from the trained center of defect-free samples. The proposed CAE model with regularizations has been tested on a variety of material surfaces, including textural and patterned surfaces in images. The experimental results reveal that the proposed CAE with regularizations significantly outperforms the conventional CAE for defect detection applications in the industry.

在本文中，评估了用于制造中自动缺陷检测的无监督自动编码器学习，其中仅无缺陷样本就可以用于模型训练。卷积自动编码器（CAE）模型的损失函数仅旨在最大程度地减少重构误差，并使代表性特征广泛传播。这项研究中提出的CAE结合了一种正则化方法，可在狭窄范围内改善无缺陷样品的特征分布。它使所有训练样本的代表性特征向量尽可能接近均值特征向量，从而使评估阶段的缺陷样本可以与经过训练的无缺陷样本中心产生明显的距离。拟议的带有正则化的CAE模型已经在各种材料表面上进行了测试，包括图像中的纹理和图案化表面。实验结果表明，所提出的带有正则化的CAE明显优于传统CAE在工业缺陷检测中的应用。