Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detection

TL;DR

This paper introduces a novel Progressive Boundary-guided Anomaly Synthesis (PBAS) strategy for industrial anomaly detection that synthesizes anomalies without auxiliary textures, improving detection accuracy and speed.

Contribution

The proposed PBAS method innovatively synthesizes feature-level anomalies directionally without relying on auxiliary datasets, enhancing industrial defect detection.

Findings

Achieves state-of-the-art performance on MVTec AD, VisA, and MPDD datasets.

Provides the fastest detection speed among compared methods.

Effectively captures useful anomaly information with reduced redundancy.

Abstract

Unsupervised anomaly detection methods can identify surface defects in industrial images by leveraging only normal samples for training. Due to the risk of overfitting when learning from a single class, anomaly synthesis strategies are introduced to enhance detection capability by generating artificial anomalies. However, existing strategies heavily rely on anomalous textures from auxiliary datasets. Moreover, their limitations in the coverage and directionality of anomaly synthesis may result in a failure to capture useful information and lead to significant redundancy. To address these issues, we propose a novel Progressive Boundary-guided Anomaly Synthesis (PBAS) strategy, which can directionally synthesize crucial feature-level anomalies without auxiliary textures. It consists of three core components: Approximate Boundary Learning (ABL), Anomaly Feature Synthesis (AFS), and Refined Boundary Optimization (RBO). To make the distribution of normal samples more compact, ABL first learns an approximate decision boundary by center constraint, which improves the center initialization through feature alignment. AFS then directionally synthesizes anomalies with more flexible scales guided by the hypersphere distribution of normal features. Since the boundary is so loose that it may contain real anomalies, RBO refines the decision boundary through the binary classification of artificial anomalies and normal features. Experimental results show that our method achieves state-of-the-art performance and the fastest detection speed on three widely used industrial datasets, including MVTec AD, VisA, and MPDD. The code will be available at: https://github.com/cqylunlun/PBAS.