A Two-Stage Deep-Learning Framework for Industrial Anomaly Detection: Integrating Small-Sample Semantic Segmentation and Knowledge Distillation

Abstract

This paper addresses the challenges of anomaly detection in industrial components by proposing a two-stage deep-learning approach combining semantic segmentation and knowledge distillation. Traditional methods, such as manual inspection and machine vision, face limitations in efficiency and accuracy when dealing with complex defects. To overcome these issues, we first introduce a small-sample semantic segmentation model based on a U-Net architecture, enhanced with an Adaptive Multi-Scale Attention Module (AMAM) and gate attention mechanisms to improve edge detection and multi-scale feature extraction. The second stage employs a knowledge distillation-based anomaly detection model, where a pre-trained teacher network (WideResNet50) extracts features, and a student network reconstructs them, with differences indicating anomalies. A Transformer-based feature aggregation module further refines the process. Experiments on the MVTec dataset demonstrate superior performance, with the segmentation model achieving 96.4% mIoU and the anomaly detection model attaining 98.3% AUC, outperforming State-of-the-Art methods. Under an extremely small-sample regime of merely 27 training images, the proposed model still attains a mIoU exceeding 94%. The two-stage approach significantly enhances detection accuracy by reducing background interference and focusing on localized defects. This work contributes to industrial quality control by improving efficiency, reducing false positives, and adapting to limited annotated data.