Teacher Encoder-Student Decoder Denoising Guided Segmentation Network for Anomaly Detection

• URL: http://arxiv.org/abs/2501.12104v3
• Date: Fri, 24 Jan 2025 08:20:19 GMT
• Title: Teacher Encoder-Student Decoder Denoising Guided Segmentation Network for Anomaly Detection
• Authors: Shixuan Song, Hao Chen, Shu Hu, Xin Wang, Jinrong Hu, Xi Wu,
• Abstract summary: We propose a novel model named PFADSeg, which integrates a pre-trained teacher network, a denoising student network with multi-scale feature fusion, and a guided anomaly segmentation network into a unified framework. evaluated on the MVTec AD dataset, PFADSeg achieves state-of-the-art results with an image-level AUC of 98.9%, a pixel-level mean precision of 76.4%, and an instance-level mean precision of 78.7%.
• Score: 15.545036112870841
• License:
• Abstract: Visual anomaly detection is a highly challenging task, often categorized as a one-class classification and segmentation problem. Recent studies have demonstrated that the student-teacher (S-T) framework effectively addresses this challenge. However, most S-T frameworks rely solely on pre-trained teacher networks to guide student networks in learning multi-scale similar features, overlooking the potential of the student networks to enhance learning through multi-scale feature fusion. In this study, we propose a novel model named PFADSeg, which integrates a pre-trained teacher network, a denoising student network with multi-scale feature fusion, and a guided anomaly segmentation network into a unified framework. By adopting a unique teacher-encoder and student-decoder denoising mode, the model improves the student network's ability to learn from teacher network features. Furthermore, an adaptive feature fusion mechanism is introduced to train a self-supervised segmentation network that synthesizes anomaly masks autonomously, significantly increasing detection performance. Evaluated on the MVTec AD dataset, PFADSeg achieves state-of-the-art results with an image-level AUC of 98.9%, a pixel-level mean precision of 76.4%, and an instance-level mean precision of 78.7%.

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