DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection
This work addresses anomaly detection for industrial inspection, offering incremental improvements over existing student-teacher methods.
The authors tackled visual anomaly detection by proposing DeSTSeg, an improved student-teacher framework that integrates denoising and segmentation, achieving state-of-the-art results of 98.6% image-level AUC, 75.8% pixel-level AP, and 76.4% instance-level AP on an industrial inspection benchmark.
Visual anomaly detection, an important problem in computer vision, is usually formulated as a one-class classification and segmentation task. The student-teacher (S-T) framework has proved to be effective in solving this challenge. However, previous works based on S-T only empirically applied constraints on normal data and fused multi-level information. In this study, we propose an improved model called DeSTSeg, which integrates a pre-trained teacher network, a denoising student encoder-decoder, and a segmentation network into one framework. First, to strengthen the constraints on anomalous data, we introduce a denoising procedure that allows the student network to learn more robust representations. From synthetically corrupted normal images, we train the student network to match the teacher network feature of the same images without corruption. Second, to fuse the multi-level S-T features adaptively, we train a segmentation network with rich supervision from synthetic anomaly masks, achieving a substantial performance improvement. Experiments on the industrial inspection benchmark dataset demonstrate that our method achieves state-of-the-art performance, 98.6% on image-level AUC, 75.8% on pixel-level average precision, and 76.4% on instance-level average precision.