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Image-based systems have gained popularity owing to their capacity to providerich manufacturing status information, low implementation costs and highacquisition rates. However, the complexity of the image background and variousanomaly patterns pose new challenges to existing matrix decomposition methods,which are inadequate for modeling requirements. Moreover, the uncertainty ofthe anomaly can cause anomaly contamination problems, making the designed modeland method highly susceptible to external disturbances. To address thesechallenges, we propose a two-stage strategy anomaly detection method thatdetects anomalies by identifying suspected patches (Ano-SuPs). Specifically, wepropose to detect the patches with anomalies by reconstructing the input imagetwice: the first step is to obtain a set of normal patches by removing thosesuspected patches, and the second step is to use those normal patches to refinethe identification of the patches with anomalies. To demonstrate itseffectiveness, we evaluate the proposed method systematically throughsimulation experiments and case studies. We further identified the keyparameters and designed steps that impact the model's performance andefficiency.

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