A Multi-Scale A Contrario method for Unsupervised Image Anomaly Detection

TL;DR

This paper introduces a multi-scale, unsupervised a contrario framework for image anomaly detection that leverages statistical analysis of feature maps from various filters, achieving state-of-the-art results across diverse scenarios.

Contribution

It presents a novel multi-scale, fully unsupervised anomaly detection method using an a contrario approach on feature maps from different filters, including deep neural networks.

Findings

Effective in detecting subtle defects in leather samples.

Achieves state-of-the-art results on public anomaly datasets.

Versatile across various application scenarios.

Abstract

Anomalies can be defined as any non-random structure which deviates from normality. Anomaly detection methods reported in the literature are numerous and diverse, as what is considered anomalous usually varies depending on particular scenarios and applications. In this work we propose an a contrario framework to detect anomalies in images applying statistical analysis to feature maps obtained via convolutions. We evaluate filters learned from the image under analysis via patch PCA, Gabor filters and the feature maps obtained from a pre-trained deep neural network (Resnet). The proposed method is multi-scale and fully unsupervised and is able to detect anomalies in a wide variety of scenarios. While the end goal of this work is the detection of subtle defects in leather samples for the automotive industry, we show that the same algorithm achieves state of the art results in public anomalies datasets.