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  4. DirtNet: Visual Dirt Detection for Autonomous Cleaning Robots
 
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2020
  • Konferenzbeitrag

Titel

DirtNet: Visual Dirt Detection for Autonomous Cleaning Robots

Abstract
Visual dirt detection is becoming an important capability of modern professional cleaning robots both for optimizing their wet cleaning results and for facilitating demand-oriented daily vacuum cleaning. This paper presents a robust, fast, and reliable dirt and office item detection system for these tasks based on an adapted YOLOv3 framework. Its superiority over state-of-the-art dirt detection systems is demonstrated in several experiments. The paper furthermore features a dataset generator for creating any number of realistic training images from a small set of real scene, dirt, and object examples.
Author(s)
Bormann, Richard
Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA
Wang, Xinjie
Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA
Xu, Jiawen
Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA
Schmidt, Joel
Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA
Hauptwerk
IEEE International Conference on Robotics and Automation, ICRA 2020
Konferenz
International Conference on Robotics and Automation (ICRA) 2020
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DOI
10.1109/ICRA40945.2020.9196559
Language
Englisch
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IPA
Tags
  • robotic dirt detectio...

  • Autonomer Mobiler Rob...

  • Reinigungsroboter

  • Schmutzerkennung

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