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Dataset . 2023
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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Hazards&Robots: A Dataset for Visual Anomaly Detection in Robotics

Authors: Mantegazza, Dario; Xhyra, Alind; Gambardella, Luca M.; Giusti, Alessandro; Guzzi, Jerome;

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

Abstract

This is the final version of our dataset; we further expand the Corridor scenario. This new version of Corridor includes 20 anomalies and the total frames are 324,408. In this version, we release feature embeddings extracted using a CLIP ViT-B/32 model. This dataset is part of a Data in Brief paper submission. For more information check https://github.com/idsia-robotics/hazard-detection

V3.1 of the Corridor scenario is released as the revised principal material for a Data in Brief paper

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!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
1
Average
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Average
99
56
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