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Multi-Perspective Anomaly Detection

 
: Jakob, Peter; Madan, Manav; Schmid-Schirling, Tobias; Valada, Abhinav

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Volltext ()

Sensors. Online journal 21 (2021), Nr.16, Art. 5311, 20 S.
https://www.mdpi.com/journal/sensors
ISSN: 1424-8220
ISSN: 1424-8239
ISSN: 1424-3210
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IPM ()
One-Class Learning; data fusion; Multi-Perspective; anomaly detection; novelty detection; deep learning

Abstract
Anomaly detection is a critical problem in the manufacturing industry. In many applications, images of objects to be analyzed are captured from multiple perspectives which can be exploited to improve the robustness of anomaly detection. In this work, we build upon the deep support vector data description algorithm and address multi-perspective anomaly detection using three different fusion techniques, i.e., early fusion, late fusion, and late fusion with multiple decoders. We employ different augmentation techniques with a denoising process to deal with scarce one-class data, which further improves the performance (ROC AUC =80%). Furthermore, we introduce the dices dataset, which consists of over 2000 grayscale images of falling dices from multiple perspectives, with 5% of the images containing rare anomalies (e.g., drill holes, sawing, or scratches). We evaluate our approach on the new dices dataset using images from two different perspectives and also benchmark on the standard MNIST dataset. Extensive experiments demonstrate that our proposed multi-perspective approach exceeds the state-of-the-art single-perspective anomaly detection on both the MNIST and dices datasets. To the best of our knowledge, this is the first work that focuses on addressing multi-perspective anomaly detection in images by jointly using different perspectives together with one single objective function for anomaly detection.

: http://publica.fraunhofer.de/dokumente/N-638633.html