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 controlled product quality with high precision. In general, automation based on computer vision is 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. Hence, this paper proposes a defect localizing autoencoder with unsupervised class selection by clustering with k-means the features extracted from a pre-trained VGG16 network. Moreover, 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.

翻译：制造业需要以高效率和大批量生产高质量成品。在工业4.0的背景下，视觉异常检测为高精度自动质量控制提供了乐观的解决方案。通常，基于计算机视觉的自动化是防止产品质量检测环节瓶颈的有前景方案。我们考虑了机器学习的最新进展以改进视觉缺陷定位，但在获取生产线中各类缺陷的均衡特征集和数据库方面仍存在挑战。因此，本文提出一种缺陷定位自编码器，通过利用k-means对预训练VGG16网络提取的特征进行聚类实现无监督类别选择。此外，选定的缺陷类别通过融合自然野生纹理进行数据增强以模拟人工缺陷。研究表明，该缺陷定位自编码器结合无监督类别选择可有效提升制造业的缺陷检测能力。所提方法在家具工业三聚氰胺板质量缺陷定位中展现出精确且准确的成果。将人工缺陷融入训练数据在实际质量控制场景中显示出显著的应用潜力。