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DirtNet: Visual Dirt Detection for Autonomous Cleaning Robots

: Bormann, Richard; Wang, Xinjie; Xu, Jiawen; Schmidt, Joel


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Robotics and Automation Society:
IEEE International Conference on Robotics and Automation, ICRA 2020 : 31 May - 31 August 2020, Virtuell, Paris, France
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-7281-7395-5
ISBN: 978-1-7281-7394-8
ISBN: 978-1-7281-7396-2
International Conference on Robotics and Automation (ICRA) <2020, Online>
Fraunhofer IPA ()
robotic dirt detection; Autonomer Mobiler Roboter; Reinigungsroboter; Schmutzerkennung

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.