Abstract
Avoiding gray water remainders behind wet floor cleaning machines is an essential requirement for safety of passersby and quality of cleaning results. Nevertheless, operators of scrubber dryers frequently do not pay sufficient attention to this aspect and automatic robotic cleaners cannot even sense water leakage. This paper introduces a compact, low-cost, low-energy water streak detection system for the use with existing and new cleaning machines. It comprises a Raspberry Pi with an Intel Movidius Neural Compute Stick, an illumination source, and a camera to observe the floor after cleaning. The paper evaluates six different Convolutional Neural Network (CNN) architectures on a self-recorded water streak data set which contains nearly 43000 images of 59 different floor types. The results show that up to 97% of all water events can be detected at a low false positive rate of only 2.6%. The fastest CNN Squeezenet can process images at a sufficient speed of over 30 Hz on the low-cost hardware such that real applicability in practice is provided. When using an NVidia Jetson Nano as alternative low-cost computing system, five out of the six networks can be operated faster than 30 Hz.
This project has received funding from the German Federal Ministry of Economic Affairs and Energy (BMWi) through grant 01MT16002A (BakeR).
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Notes
- 1.
The NVidia Jetson Nano device was not available by the time the project was started. Hence, the Raspberry Pi and Intel Movidius Stick were originally employed as the standard setup for this work.
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Jost, U., Bormann, R. (2019). Water Streak Detection with Convolutional Neural Networks for Scrubber Dryers. In: Tzovaras, D., Giakoumis, D., Vincze, M., Argyros, A. (eds) Computer Vision Systems. ICVS 2019. Lecture Notes in Computer Science(), vol 11754. Springer, Cham. https://doi.org/10.1007/978-3-030-34995-0_24
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