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Related Experiment Video

Updated: Jul 25, 2025

Design and Analysis for Fall Detection System Simplification
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Hazards&Robots: A dataset for visual anomaly detection in robotics.

Dario Mantegazza1, Alind Xhyra1, Luca M Gambardella1

  • 1IDSIA - Dalle Molle Institute for Artificial Intelligence USI-SUPSI, Polo universitario Lugano - Campus Est, Via la Santa 1, CH-6962 Lugano-Viganello.

Data in Brief
|June 29, 2023
PubMed
Summary

We introduce Hazards&Robots, a large dataset for visual anomaly detection in robotics. This dataset aids in training and testing deep learning models for identifying anomalies like unexpected objects or robot defects.

Keywords:
Computer visionDeep learningIntelligent roboticsOut of distribution detection

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Area of Science:

  • Robotics
  • Computer Vision
  • Machine Learning

Background:

  • Visual anomaly detection is crucial for robot safety and reliability.
  • Existing datasets may not adequately represent real-world robotic operational hazards.

Purpose of the Study:

  • To introduce the Hazards&Robots dataset for visual anomaly detection in robotics.
  • To provide a comprehensive resource for training and evaluating anomaly detection models.

Main Methods:

  • The dataset comprises 324,408 RGB frames from a DJI Robomaster S1 robot.
  • It includes 145,470 normal frames and 178,938 anomalous frames across 20 categories.
  • Anomalies include human presence, floor obstacles, and robot defects.

Main Results:

  • The dataset facilitates the development of advanced visual anomaly detection algorithms.
  • It enables benchmarking of deep learning vision models for robotic applications.

Conclusions:

  • Hazards&Robots offers a valuable resource for advancing research in robotic safety and perception.
  • The dataset supports the creation of more robust and reliable autonomous systems.