Ano-SuPs: Multi-size anomaly detection for manufactured products by identifying suspected patches

TL;DR

This paper introduces Ano-SuPs, a two-stage anomaly detection method for manufactured products using image analysis, which reconstructs images to identify suspicious patches and improve detection accuracy amid complex backgrounds.

Contribution

The paper presents a novel two-stage patch-based anomaly detection approach that effectively handles complex backgrounds and anomaly uncertainty in manufacturing images.

Findings

Effective detection of anomalies in complex backgrounds

Improved accuracy through double reconstruction of images

Key parameters influencing performance identified

Abstract

Image-based systems have gained popularity owing to their capacity to provide rich manufacturing status information, low implementation costs and high acquisition rates. However, the complexity of the image background and various anomaly patterns pose new challenges to existing matrix decomposition methods, which are inadequate for modeling requirements. Moreover, the uncertainty of the anomaly can cause anomaly contamination problems, making the designed model and method highly susceptible to external disturbances. To address these challenges, we propose a two-stage strategy anomaly detection method that detects anomalies by identifying suspected patches (Ano-SuPs). Specifically, we propose to detect the patches with anomalies by reconstructing the input image twice: the first step is to obtain a set of normal patches by removing those suspected patches, and the second step is to use those normal patches to refine the identification of the patches with anomalies. To demonstrate its effectiveness, we evaluate the proposed method systematically through simulation experiments and case studies. We further identified the key parameters and designed steps that impact the model's performance and efficiency.