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Water Streak Detection with Convolutional Neural Networks for Scrubber Dryers

  • Uriel Jost
  • Richard BormannEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11754)

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.

Keywords

Water streak detection Cleaning robots Liquid sensing 

References

  1. 1.
  2. 2.
    Comac Ultra 100 BS AS, product image, KENTER GmbH. https://www.kenter.de/components/com_mijoshop/opencart/image/cache/data/35052-1500x1135.jpg. Accessed 22 July 2019
  3. 3.
    Bormann, R., Weisshardt, F., Arbeiter, G., Fischer, J.: Autonomous dirt detection for cleaning in office environments. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 1252–1259 (2013)Google Scholar
  4. 4.
    Bormann, R., Fischer, J., Arbeiter, G., Weißhardt, F., Verl, A.: A visual dirt detection system for mobile service robots. In: Proceedings of the 7th German Conference on Robotics (ROBOTIK 2012), Munich, Germany, May 2012Google Scholar
  5. 5.
    Bormann, R., Hampp, J., Hägele, M.: New brooms sweep clean - an autonomous robotic cleaning assistant for professional office cleaning. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), May 2015Google Scholar
  6. 6.
    Bormann, R., Jordan, F., Hampp, J., Hägele, M.: Indoor coverage path planning: survey, implementation, analysis. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). pp. 1718–1725, May 2018Google Scholar
  7. 7.
    Bossu, J., Hautière, N., Tarel, J.P.: Rain or snow detection in image sequences through use of a histogram of orientation of streaks. Int. J. Comput. Vis. 93(3), 348–367 (2011)CrossRefGoogle Scholar
  8. 8.
    Chen, J., Tan, C., Hou, J., Chau, L., Li, H.: Robust video content alignment and compensation for rain removal in a CNN framework. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6286–6295, June 2018Google Scholar
  9. 9.
    Endres, H., Feiten, W., Lawitzky, G.: Field test of a navigation system: autonomous cleaning in supermarkets. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 1779–1781, May 1998Google Scholar
  10. 10.
    Grünauer, A., Halmetschlager-Funek, G., Prankl, J., Vincze, M.: Learning the floor type for automated detection of dirt spots for robotic floor cleaning using gaussian mixture models. In: Liu, M., Chen, H., Vincze, M. (eds.) ICVS 2017. LNCS, vol. 10528, pp. 576–589. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-68345-4_51CrossRefGoogle Scholar
  11. 11.
    Grünauer, A., Halmetschlager-Funek, G., Prankl, J., Vincze, M.: The power of GMMs: unsupervised dirt spot detection for industrial floor cleaning robots. In: Gao, Y., Fallah, S., Jin, Y., Lekakou, C. (eds.) TAROS 2017. LNCS (LNAI), vol. 10454, pp. 436–449. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-64107-2_34CrossRefGoogle Scholar
  12. 12.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)Google Scholar
  13. 13.
    Hess, J., Beinhofer, M., Burgard, W.: A probabilistic approach to high-confidence cleaning guarantees for low-cost cleaning robots. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2014)Google Scholar
  14. 14.
    Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269, July 2017Google Scholar
  15. 15.
    Iandola, F.N., Moskewicz, M.W., Ashraf, K., Han, S., Dally, W.J., Keutzer, K.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and \(<\)1mb model size. CoRR (2016)Google Scholar
  16. 16.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)Google Scholar
  17. 17.
    Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 122–138. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01264-9_8CrossRefGoogle Scholar
  18. 18.
    Paszke, A., et al.: Automatic differentiation in PyTorch. In: NIPS Autodiff Workshop (2017)Google Scholar
  19. 19.
    Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4510–4520 June 2018Google Scholar
  21. 21.
    Schenck, C., Fox, D.: Towards learning to perceive and reason about liquids. In: Kulić, D., Nakamura, Y., Khatib, O., Venture, G. (eds.) ISER 2016. SPAR, vol. 1, pp. 488–501. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-50115-4_43CrossRefGoogle Scholar
  22. 22.
    Schenck, C., Fox, D.: Visual closed-loop control for pouring liquids. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 2629–2636 (2017)Google Scholar
  23. 23.
    Schenck, C., Fox, D.: Perceiving and reasoning about liquids using fully convolutional networks. Int. J. Robot. Res. 37(4–5), 452–471 (2018)CrossRefGoogle Scholar
  24. 24.
    Yang, W., Tan, R.T., Feng, J., Liu, J., Guo, Z., Yan, S.: Deep joint rain detection and removal from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1685–1694, July 2017Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Fraunhofer IPA, Robot and Assistive SystemsStuttgartGermany

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