single-rb.php

JRM Vol.36 No.2 pp. 388-395
doi: 10.20965/jrm.2024.p0388
(2024)

Paper:

Water Droplet Detection System on Toilet Floor Using Heat Absorption Capacity of Liquid

Rama Okta Wiyagi*,** ORCID Icon and Kazuyoshi Wada*

*Graduate School of Systems Design, Tokyo Metropolitan University
6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

**Department of Electrical Engineering, Universitas Muhammadiyah Yogyakarta
Jl. Brawijaya, Kasihan, Bantul, Yogyakarta 55183, Indonesia

Received:
July 24, 2023
Accepted:
October 25, 2023
Published:
April 20, 2024
Keywords:
water droplet detection, toilet floor, heat absorption capacity, toilet liquid waste, low-cost thermal chessboard
Abstract

Liquid waste is a type of dirt that is often found in toilets. Detection of liquid waste such as water or urine in the restroom is challenging due to their limited physical appearances, e.g., transparency and small size. This paper proposes a new method to detect water droplets, including water splashes, on the toilet floor by using the heat absorption capacity of liquid. Water, air, and floor have different heat capacity characteristics. Increasing temperature difference between water droplets and surroundings is done using blowing air on the surface of the detection area. A thermal camera is used to observe the detection area and an adaptive threshold is implemented to localize water droplets. This study also proposed a low-cost calibration chessboard method for thermal images that can produce good contrast images for calibrating wide-angle thermal camera modules. The results obtained from the experiment were promising, the system was able to detect single water drop up to 2 mm in diameter on a floor of 90 × 170 cm, and detection rate was above 95% for water droplets with a minimal size of 5 mm in diameter.

Water droplet detection system

Water droplet detection system

Cite this article as:
R. Wiyagi and K. Wada, “Water Droplet Detection System on Toilet Floor Using Heat Absorption Capacity of Liquid,” J. Robot. Mechatron., Vol.36 No.2, pp. 388-395, 2024.
Data files:
References
  1. [1] D. Mara, J. Lane, B. Scott, and D. Trouba, “Sanitation and health,” PLOS Medicine, Vol.7, No.11, Article No.e1000363, 2010. https://doi.org/10.1371/journal.pmed.1000363
  2. [2] S. Kumar B. P. et al., “Importance of understanding the need of personal hygiene: A comprehensive review,” Int. J. of Research in Pharmacy and Pharmaceutical Sciences, Vol.5, No.6, pp. 56-61, 2020.
  3. [3] P. Simha and M. Ganesapillai, “Ecological sanitation and nutrient recovery from human urine: How far have we come? A review,” Sustainable Environment Research, Vol.27, No.3, pp. 107-116, 2017. https://doi.org/10.1016/j.serj.2016.12.001
  4. [4] X. Zhou et al., “Review of global sanitation development,” Environment Int., Vol.120, pp. 246-261, 2018. https://doi.org/10.1016/j.envint.2018.07.047
  5. [5] R. Barcan, “Dirty spaces: Communication and contamination in men’s public toilets,” J. of Int. Women’s Studies, Vol.6, No.2, pp. 7-23, 2005.
  6. [6] U. Klank, D. Carton, and M. Beetz, “Transparent object detection and reconstruction on a mobile platform,” 2011 IEEE Int. Conf. on Robotics and Automation, pp. 5971-5978, 2011.
  7. [7] G.-H. Chen, J.-Y. Wang, and A.-J. Zhang, “Transparent object detection and location based on RGB-D camera,” J. of Physics: Conf. Series, Vol.1183, Article No.012011. 2019. https://doi.org/10.1088/1742-6596/1183/1/012011
  8. [8] G. ElMasry et al., “Emerging thermal imaging techniques for seed quality evaluation: Principles and applications,” Food Research Int., Vol.131, Article No.109025, 2020.
  9. [9] S. D. Holland and R. S. Reusser, “Material evaluation by infrared thermography,” Annual Review of Materials Research, Vol.46, No.1, pp. 287-303, 2016. https://doi.org/10.1146/annurev-matsci-070115-032014
  10. [10] T. Herrmann, C. Migniot, and O. Aubreton, “Thermal camera calibration with cooled down chessboard,” Proc. of the 2020 Int. Conf. on Quantitative InfraRed Thermography Conf. (QIRT 2020), 2020. https://doi.org/10.21611/qirt.2020.010
  11. [11] A. Choinowski, D. Dahlke, I. Ernst, S. Pless, and I. Rettig, “Automatic calibration and co-registration for a stereo camera system and a thermal imaging sensor using a chessboard,” The Int. Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.XLII-2/W13, pp. 1631-1635, 2019. https://doi.org/10.5194/isprs-archives-XLII-2-W13-1631-2019
  12. [12] Y. Chen, F.-Y. Huang, F.-M. Shi, B.-Q. Liu, and H. Yu, “Plane chessboard-based calibration method for a LWIR ultra-wide-angle camera,” Applied Optics, Vol.58, No.4, pp. 744-751, 2019. https://doi.org/10.1364/AO.58.000744
  13. [13] I. N. Swamidoss, A. B. Amro, and S. Sayadi, “Systematic approach for thermal imaging camera calibration for machine vision applications,” Optik, Vol.247, Article No.168039, 2021. https://doi.org/10.1016/j.ijleo.2021.168039
  14. [14] R. Liu, H. Zhang, and S. Scherer, “Multiple methods of geometric calibration of thermal camera and a method of extracting thermal calibration feature points,” Carnegie Mellon University, 2018.
  15. [15] T. Shibata, M. Tanaka, and M. Okutomi, “Accurate joint geometric camera calibration of visible and far-infrared cameras,” Proc. of IS&T Int. Symp. on Electronic Imaging: Image Sensors and Imaging Systems, pp. 7-13, 2017. https://doi.org/10.2352/ISSN.2470-1173.2017.11.IMSE-078
  16. [16] Y. Kubota, Y. Ke, T. Hayakawa, Y. Moko, and M. Ishikawa, “Optimal material search for infrared markers under non-heating and heating conditions,” Sensors, Vol.21, No.19, Article No.6527, 2021. https://doi.org/10.3390/s21196527
  17. [17] J. A. Curcio and C. C. Petty, “The near infrared absorption spectrum of liquid water,” J. of the Optical Society of America, Vol.41, No.5, pp. 302-304, 1951. https://doi.org/10.1364/JOSA.41.000302
  18. [18] R. Gade and T. B. Moeslund, “Thermal cameras and applications: A survey,” Machine Vision and Applications, Vol.25, No.1, pp. 245-262, 2014. https://doi.org/10.1007/s00138-013-0570-5
  19. [19] X. Li et al., “A novel attentive generative adversarial network for waterdrop detection and removal of rubber conveyor belt image,” Mathematical Problems in Engineering, Vol.2020, Article No.1037021, 2020. https://doi.org/10.1155/2020/1037021
  20. [20] H.-C. Liao, D.-Y. Wang, C.-L. Yang, and J. Shin, “Video-based water drop detection and removal method for a moving vehicle,” Information Technology J., Vol.12, No.4, pp. 569-583, 2013. https://doi.org/10.3923/itj.2013.569.583
  21. [21] D. Schönauer and R. Moos, “Detection of water droplets on exhaust gas sensors,” Sensors and Actuators B: Chemical, Vol.148, No.2, pp. 624-629, 2010. https://doi.org/10.1016/j.snb.2010.05.060
  22. [22] J. Kim, J. Baek, H. Choi, and E. Kim, “Wet area and puddle detection for Advanced Driver Assistance Systems (ADAS) using a stereo camera,” Int. J. of Control, Automation and Systems, Vol.14, No.1, pp. 263-271, 2016. https://doi.org/10.1007/s12555-015-0024-0
  23. [23] M. Yamada, K. Ueda, I. Horiba, S. Yamamoto, and S. Tsugawa, “Detection of wet-road conditions from images captured by a vehicle-mounted camera,” J. Robot. Mechatron., Vol.17, No.3, pp. 269-276, 2005. https://doi.org/10.20965/jrm.2005.p0269
  24. [24] H. Zhu and Y. Yamakawa, “Robotic pouring based on real-time observation and visual feedback by a high-speed vision system,” J. Robot. Mechatron., Vol.34, No.5, pp. 965-974, 2022. https://doi.org/10.20965/jrm.2022.p0965
  25. [25] E. P. Carreño-Alvarado and G. R. Meza, “Water leak detection by termographic image analysis, in laboratory tests,” Proceedings, Vol.48, No.1, Article No.15, 2019.
  26. [26] B. Shakmak and A. Al-Habaibeh, “Detection of water leakage in buried pipes using infrared technology; A comparative study of using high and low resolution infrared cameras for evaluating distant remote detection,” 2015 IEEE Jordan Conf. on Applied Electrical Engineering and Computing Technologies (AEECT), 2015. https://doi.org/10.1109/AEECT.2015.7360563
  27. [27] C. Penteado et al., “Water leaks detection based on thermal images,” 2018 IEEE Int. Smart Cities Conf. (ISC2), 2018. https://doi.org/10.1109/ISC2.2018.8656938
  28. [28] R. K. Parida, V. Thyagarajan, and S. Menon, “A thermal imaging based wireless sensor network for automatic water leakage detection in distribution pipes,” 2013 IEEE Int. Conf. on Electronics, Computing and Communication Technologies, 2013. https://doi.org/10.1109/CONECCT.2013.6469289
  29. [29] C. Devaguptapu, N. Akolekar, M. M. Sharma, and V. N. Balasubramanian, “Borrow from anywhere: Pseudo multi-modal object detection in thermal imagery,” 2019 IEEE/CVF Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1029-1038, 2019. https://doi.org/10.1109/CVPRW.2019.00135
  30. [30] G. Batchuluun et al., “Deep learning-based thermal image reconstruction and object detection,” IEEE Access, Vol.9, pp. 5951-5971, 2020. https://doi.org/10.1109/ACCESS.2020.3048437

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jun. 19, 2024