UzADL: Anomaly detection and localization using graph Laplacian matrix-based unsupervised learning method

Visual inspection is an essential quality control process in industrial businesses. It is usually automated due to its tedious procedure. An automated visual inspection (AVI) attempts to detect items with abnormal patterns based on image data. Recent developments in computer vision models, especially the introduction of deep convolutional neural networks, has extensively improved the accuracy and speed of AVI systems. However, supervised learning approaches for AVI necessitate a large number of annotated data, while the unsupervised ones lack accuracy and interpretability as well as require an extensive amount of time for training and inference. Therefore, in this study, we propose an unsupervised learning-based computationally inexpensive, efficient, and interpretable model UzADL for AVI to address the aforementioned problems. This system has three principal stages. First, unlabeled images are annotated using a pseudo-labeling algorithm. Second, the obtained instances are trained during a training process stage. Third, identified abnormal instances’ defective regions are explicitly visualized using an anomaly interpretation technique. Owing to an elaborate unsupervised learning method based on the pseudo-labeling algorithm using graph Laplacian matrix that allows transforming defect detection into a classification task, the proposed system has rapid convergence ability and significantly outperforms existing deep learning-based AVI methods. In the experiments conducted with three real-life fabric material databases NanoTWICE, MVTec anomaly detection (MVTec AD), and DWorld datasets UzADL outperformed other methods in terms of accuracy and speed when assessed using several evaluation metrics.