Manufacturing industries require efficient and voluminous production of
high-quality finished goods. In the context of Industry 4.0, visual anomaly
detection poses an optimistic solution for automatically controlling product
quality with high precision. Automation based on computer vision poses a
promising solution to prevent bottlenecks at the product quality checkpoint. We
considered recent advancements in machine learning to improve visual defect
localization, but challenges persist in obtaining a balanced feature set and
database of the wide variety of defects occurring in the production line. This
paper proposes a defect localizing autoencoder with unsupervised class
selection by clustering with k-means the features extracted from a pre-trained
VGG-16 network. The selected classes of defects are augmented with natural wild
textures to simulate artificial defects. The study demonstrates the
effectiveness of the defect localizing autoencoder with unsupervised class
selection for improving defect detection in manufacturing industries. The
proposed methodology shows promising results with precise and accurate
localization of quality defects on melamine-faced boards for the furniture
industry. Incorporating artificial defects into the training data shows
significant potential for practical implementation in real-world quality
control scenarios.

基于视觉异常检测和无监督类别选择的缺陷定位自编码器在制造业中提高缺陷检测的准确度和可靠性。