Self-Supervised Guided Segmentation Framework for Unsupervised Anomaly Detection

TL;DR

This paper introduces SGSF, a self-supervised framework that generates forged anomalies and leverages normal features for effective unsupervised anomaly detection in industrial settings.

Contribution

The paper proposes a novel self-supervised guided segmentation framework combining forged anomaly generation and normal feature guidance for improved unsupervised anomaly detection.

Findings

Achieves state-of-the-art results on three datasets.

Effective generation of forged anomalies via Saliency Augmentation Module.

Normal feature guidance enhances anomaly localization accuracy.

Abstract

Unsupervised anomaly detection is a challenging task in industrial applications since it is impracticable to collect sufficient anomalous samples. In this paper, a novel Self-Supervised Guided Segmentation Framework (SGSF) is proposed by jointly exploring effective generation method of forged anomalous samples and the normal sample features as the guidance information of segmentation for anomaly detection. Specifically, to ensure that the generated forged anomaly samples are conducive to model training, the Saliency Augmentation Module (SAM) is proposed. SAM introduces a saliency map to generate saliency Perlin noise map, and develops an adaptive segmentation strategy to generate irregular masks in the saliency region. Then, the masks are utilized to generate forged anomalous samples as negative samples for training. Unfortunately, the distribution gap between forged and real anomaly samples makes it difficult for models trained based on forged samples to effectively locate real anomalies. Towards this end, the Self-supervised Guidance Network (SGN) is proposed. It leverages the self-supervised module to extract features that are noise-free and contain normal semantic information as the prior knowledge of the segmentation module. The segmentation module with the knowledge of normal patterns segments out the abnormal regions that are different from the guidance features. To evaluate the effectiveness of SGSF for anomaly detection, extensive experiments are conducted on three anomaly detection datasets. The experimental results show that SGSF achieves state-of-the-art anomaly detection results.