Adaptive Context-Aware Distillation for Industrial Image Anomaly Detection

Image anomaly detection is extremely challenging in industrial manufacturing processes due to unforeseen and diversified anomalies. Recently, unsupervised anomaly detection methods based on knowledge distillation (KD) have been developed and have shown remarkable potential. While most existing methods are devoted to knowledge generalization, they are inadequate for the fine-grained detection task. To address this issue, we propose a novel adaptive context-aware distillation (ACAD) paradigm that gives due consideration to distillation component dependencies and knowledge transfer optimization. Technically, a novel adaptive distillation module (ADM) is proposed for optimal context-aware knowledge transfer, which consists of contrastive decoupling distillation (CDD) and masked perceiving distillation (MPD). The proposed CDD helps to constrain the distribution of different semantic patterns and strengthen the discriminative capability. Vanilla methods treat every pixel as an equal contribution and fail to focus on critical information. To this end, the MPD is proposed to weigh different contextual knowledge adaptively. Extensive experiments with mainstream anomaly detection datasets show that ACAD outperforms state-of-the-art competitors in accuracy and efficiency. In addition, the experimental results with a real-world inkjet printing organic electroluminescence display (OLED) panel dataset further demonstrate the effectiveness of our method.