DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection

By Xuan Zhang, Shiyu Li, Xi Li, Ping-Chia Huang, Jiulong Shan, Ting Chen. Computer Vision and Pattern Recognition, 2022

337 citations recorded by the source corpus.

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 chal-lenge. However, previous works based on S-T only empirically applied constraints on normal data and fused multilevel information. In this study, we propose an improved model called DeS TSeg, 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 intro-duce a denoising procedure that allows the student net-work to learn more robust representations. From synthet-ically corrupted normal images, we train the student net-work 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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/d17df33c9b6453d61d01353e94592f1757caee8a