A Contrario multi-scale anomaly detection method for industrial quality inspection
This work addresses anomaly detection for industrial quality inspection, particularly for subtle defects in leather samples in the automotive industry, but it is incremental as it builds on existing methods like PCA and pre-trained networks.
The authors tackled the problem of detecting anomalies in images by proposing an a contrario framework that applies statistical analysis to feature maps from convolutions, achieving state-of-the-art results on public datasets.
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.