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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
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Hazards&Robots: A Dataset for Visual Anomaly Detection in Robotics

Research datakeyboard_double_arrow_right Dataset 13 Sep 2022 English Publisher:ZenodoFunded by:EC | 1-SWARM
Authors: Mantegazza, Dario; Giusti, Alessandro; Gambardella, Luca M.; Guzzi, Jerome;

doi: 10.5281/zenodo.7074958

Hazards&Robots: A Dataset for Visual Anomaly Detection in Robotics

Abstract

We expand our previous dataset with an extended version of the Corridor scenario. This new version of Corridor includes 16 anomalies and 132'838 frames. This dataset was used in the paper "An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots."

V2 of the Corridor scenario is released as supplementary material for the paper https://ieeexplore.ieee.org/document/9834998

Keywords

Deep Learning, Anomaly Detection, Robot Vision, Out of Distribution Detection

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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