Unsupervised anomaly detection for manufacturing product images by significant feature space distance measurement

Abstract

Using camera instruments to detect product anomalies is increasingly being applied in various manufacturing scenarios. Detection of anomaly product images and identification of anomaly areas can not only ensure product quality, but also help reveal potential problems in the manufacturing process. Unsupervised data environments and diverse engineering requirements are two challenges faced by modern manufacturing. To solve the problem of manufacturing product anomaly detection in the absence of anomaly data, and meet the requirements of model training cost, detection speed and accuracy, a fast deployable unsupervised image anomaly detection method is proposed without parameter training. The main idea is to design an efficient and training-free anomaly detection decoder. The encoder output is screened by non-parametric method, and the image anomaly score is calculated by the cumulative distance of the screened feature space. In addition, the proposed model is able to give the reference samples on which the prediction results are based to improve the interpretability of the predictions. Experiments are conducted with 15 real product image datasets, and the results show that the proposed method achieves performance improvement over other methods for both anomaly detection and segmentation.