Biomimetic Visual Perception Network for Industrial Image Anomaly Detection

Anomaly detection in the industrial manufacturing process is important for controlling product quality. In real-world scenarios, anomalies manifest in a number of diverse and unforeseen ways and can be categorized into two main types: structural anomalies, such as scratches and breakages, and logical anomalies, such as missing components and dislocations. Current mainstream methods tend to focus on knowledge generalization but are inadequate for detailed and logical detection tasks. To address this issue, this study proposes Biomimetic Visual Perception Network (BVPN), a biomimetic design-based novel paradigm that simulates biological vision and human logical discrimination with detailed distillation dependencies and semantic component information. BVPN introduces three convolution-based networks to extract information from different regions, imitating biological central, para-central, and peripheral vision, and facilitates the integration of disparate information through the process of knowledge distillation. On the basis, BVPN develops two modules based on the student-teacher network: Focus Module, for local detailed detection, and Peripheral Module, for global context detection. Moreover, BVPN proposes the Logical Module, a clustering-based segmentation branch, assessing logical anomalies by the number and color information of segmentation components. Through a fused structure highly aligning with the perception mode of human system, BVPN achieves excellent performance in the detection of structural and logical anomalies. Extensive experiments with mainstream anomaly detection datasets and real-world inkjet printing dataset demonstrate that BVPN outperforms state-of-the-art competitors in accuracy.