FractalAD: A simple industrial anomaly segmentation method using fractal anomaly generation and backbone knowledge distillation

by   Xuan Xia, et al.
Nanjing University of Aeronautics and Astronautics
Shanghai Jiao Tong University

Although industrial anomaly detection (AD) technology has made significant progress in recent years, generating realistic anomalies and learning priors knowledge of normal remain challenging tasks. In this study, we propose an end-to-end industrial anomaly segmentation method called FractalAD. Training samples are obtained by synthesizing fractal images and patches from normal samples. This fractal anomaly generation method is designed to sample the full morphology of anomalies. Moreover, we designed a backbone knowledge distillation structure to extract prior knowledge contained in normal samples. The differences between a teacher and a student model are converted into anomaly attention using a cosine similarity attention module. The proposed method enables an end-to-end semantic segmentation network to be used for anomaly detection without adding any trainable parameters to the backbone and segmentation head. The results of ablation studies confirmed the effectiveness of fractal anomaly generation and backbone knowledge distillation. The results of performance experiments showed that FractalAD achieved competitive results on the MVTec AD dataset compared with other state-of-the-art anomaly detection methods.


page 2

page 3

page 4

page 5

page 8


Brittle Features May Help Anomaly Detection

One-class anomaly detection is challenging. A representation that clearl...

Anomaly Detection via Reverse Distillation from One-Class Embedding

Knowledge distillation (KD) achieves promising results on the challengin...

Contextual Affinity Distillation for Image Anomaly Detection

Previous works on unsupervised industrial anomaly detection mainly focus...

Prior Knowledge Guided Network for Video Anomaly Detection

Video Anomaly Detection (VAD) involves detecting anomalous events in vid...

Exploring the Optimization Objective of One-Class Classification for Anomaly Detection

One-class classification (OCC) is a longstanding method for anomaly dete...

Asymmetric Distillation Post-Segmentation Method for Image Anomaly Detection

Knowledge distillation-based anomaly detection methods generate same out...

Anomaly Discovery in Semantic Segmentation via Distillation Comparison Networks

This paper aims to address the problem of anomaly discovery in semantic ...

Please sign up or login with your details

Forgot password? Click here to reset