The voraus-AD Dataset for Anomaly Detection in Robot Applications

Author:

Brockmann Jan Thieß1ORCID,Rudolph Marco2ORCID,Rosenhahn Bodo2ORCID,Wandt Bastian3ORCID

Affiliation:

1. voraus robotik GmbH, Hanover, Germany

2. Institute for Information Processing, L3S, Leibniz University Hannover, Hannover, Germany

3. Computer Vision Laboratory, Linköping University, Linköping, Sweden

Funder

Bundesministerium für Bildung und Forschung

AI service center KISSKI

Deutsche Forschungsgemeinschaft

Germany's Excellence Strategy

Cluster of Excellence PhoenixD

German Federal Ministry of the Environment

Nature Conservation, Nuclear Safety and Consumer Protection

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Reference55 articles.

1. Nearest-neighbor and clustering based anomaly detection algorithms for rapidminer;Amer,2012

2. Guided image generation with conditional invertible neural networks;Ardizzone,2019

3. A Minimally Supervised Approach Based on Variational Autoencoders for Anomaly Detection in Autonomous Robots

4. HMMs for anomaly detection in autonomous robots;Azzalini,2020

5. INDUSTRY 4.0: A REVIEW ON INDUSTRIAL AUTOMATION AND ROBOTIC

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