  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.
