Autoencoder-Based Visual Anomaly Localization for Manufacturing Quality Control

TL;DR

This paper introduces an autoencoder-based visual anomaly localization method for manufacturing quality control, utilizing unsupervised class selection and data augmentation to improve defect detection accuracy.

Contribution

It proposes a novel defect localization approach combining autoencoders, unsupervised class selection via clustering, and artificial defect augmentation for enhanced manufacturing defect detection.

Findings

Effective localization of defects on furniture boards

Improved detection accuracy with artificial defect augmentation

Promising results for real-world manufacturing quality control

Abstract

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.