Bormann, RichardRichardBormannWang, XinjieXinjieWangXu, JiawenJiawenXuSchmidt, JoelJoelSchmidt2022-03-142022-03-142020https://publica.fraunhofer.de/handle/publica/40928510.1109/ICRA40945.2020.9196559Visual 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.enrobotic dirt detectionAutonomer Mobiler RoboterReinigungsroboterSchmutzerkennungconference paper