SPACE: SPAtial-aware Consistency rEgularization for anomaly detection in Industrial applications
This addresses anomaly detection for industrial quality control, representing an incremental improvement with novel regularization techniques.
The paper tackled the problem of anomaly detection in industrial applications by proposing SPACE, a method that integrates a Feature Encoder into the Student-Teacher framework with Spatial Consistency regularization Loss and Feature converter Module, achieving superior detection results compared to state-of-the-art methods on MVTec LOCO, MVTec AD, and VisA datasets.
In this paper, we propose SPACE, a novel anomaly detection methodology that integrates a Feature Encoder (FE) into the structure of the Student-Teacher method. The proposed method has two key elements: Spatial Consistency regularization Loss (SCL) and Feature converter Module (FM). SCL prevents overfitting in student models by avoiding excessive imitation of the teacher model. Simultaneously, it facilitates the expansion of normal data features by steering clear of abnormal areas generated through data augmentation. This dual functionality ensures a robust boundary between normal and abnormal data. The FM prevents the learning of ambiguous information from the FE. This protects the learned features and enables more effective detection of structural and logical anomalies. Through these elements, SPACE is available to minimize the influence of the FE while integrating various data augmentations.In this study, we evaluated the proposed method on the MVTec LOCO, MVTec AD, and VisA datasets. Experimental results, through qualitative evaluation, demonstrate the superiority of detection and efficiency of each module compared to state-of-the-art methods.