single-au.php

IJAT Vol.16 No.6 pp. 766-772
doi: 10.20965/ijat.2022.p0766
(2022)

Report:

Visual Identification-Based Spark Recognition System

Tianhao Cheng*, Hao Hu**, Hitoshi Kobayashi**, and Hiroshi Onoda*,†

*Graduate School of Environment and Energy Engineering, Waseda University
513 Wasedatsurumakicho, Shinjuku-ku, Tokyo 162-0041, Japan

Corresponding author

**EII, Inc., Tokyo, Japan

Received:
April 27, 2022
Accepted:
August 5, 2022
Published:
November 5, 2022
Keywords:
visual recognition, fire-detection system, lithium-ion battery, early detection and extinguishing, recycling facility
Abstract

With the development of artificial intelligence, image recognition has seen wider adoption. Here, a novel paradigm image recognition system is proposed for detection of fires owing to the compression of lithium-ion batteries at recycling facilities. The proposed system uses deep learning method. The SparkEye system is proposed, focusing on the early detection of fires as sparks, and is combined with a sprinkler system, to minimize fire-related losses at affected facilities. Approximately 30,000 images (resolution, 800 × 600 pixels) were used for training the system to >90% detection accuracy. To fulfil the demand for dust control at recycling facilities, air and frame camera protection methods were incorporated into the system. Based on the test data and realistic workplace feedback, the best placements of the SparkEye fire detectors were crushers, conveyors, and garbage pits.

Cite this article as:
T. Cheng, H. Hu, H. Kobayashi, and H. Onoda, “Visual Identification-Based Spark Recognition System,” Int. J. Automation Technol., Vol.16, No.6, pp. 766-772, 2022.
Data files:
References
  1. [1] Ministry of the Environment, “Results of the study on appropriate disposal measures for lithium-ion batteries and other difficult-to-dispose-of materials in 2020 (excerpts from the work report, etc.),” 2021 (in Japanese).
  2. [2] L. Kong, C. Li, J. Jiang, and M. Pecht, “Li-Ion Battery Fire Hazards and Safety Strategies,” Energies, Vol.11, No.9, 2191, 2018.
  3. [3] W. Zhang, L. Wu, J. Du, J. Tian, Y. Li, Y. Zhao, H. Wu, Y. Zhong, Y.-C. Cao, and S. Cheng, “Fabrication of a microcapsule extinguishing agent with a core-shell structure for lithium-ion battery fire safety,” Materials Advances, Vol.2, pp. 4634-4642, 2021.
  4. [4] S. G. Kong et al., “Fast fire flame detection in surveillance video using logistic regression and temporal smoothing,” Fire Safety J., Vol.79, pp. 37-43, 2016.
  5. [5] S. J. Chen, D. C. Hovde, K. A. Peterson, and A. W. Marshall, “Fire detection using smoke and gas sensors,” Fire Saf. J., Vol.42, No.8, pp. 507-515, 2007.
  6. [6] N.-H. Quttineh, P.-M. Olsson, T. Larsson, and H. Lindell, “An optimization approach to the design of outdoor thermal fire detection systems,” Fire Safety J., Vol.129, 103548, 2022.
  7. [7] J. H. Park, S. Lee, S. Yun, H. Kim, and W.-T. Kim, “Dependable fire detection system with Multifunctional Artificial Intelligence Framework,” Sensors, Vol.19, No.9, 2025, 2019.
  8. [8] The Japan Containers and Packaging Recycling Association, “Smoke and fire problems caused by lithium-ion batteries and other ignition materials” 2022 (in Japanese). https://www.jcpra.or.jp/municipality/dangerous/tabid/757/index.php [Accessed April 9, 2022]
  9. [9] Ministry of the Environment, “Report on Investigation of Accidents at General Waste Treatment Facilities in 2018,” 2019.
  10. [10] C. Cheng, F. Sun, and X. Zhou, “One fire detection method using neural networks,” Tsinghua Science and Technology, Vol.16, No.1, pp. 31-35, 2011.
  11. [11] Z. Zhong, M. Wang, Y. Shi, and W. Gao, “A convolutional neural network-based flame detection method in video sequence,” Signal, Image and Video Processing, Vol.12, No.8, pp. 1619-1627, 2018.
  12. [12] K. Muhammad, S. Khan, M. Elhoseny, S. Hassan Ahmed, and S. Wook Baik, “Efficient fire detection for uncertain surveillance environment,” IEEE Trans. on Industrial Informatics, Vol.15, No.5, pp. 3113-3122, 2019.
  13. [13] G. Roque and V. S. Padilla, “Lpwan based IOT surveillance system for outdoor fire detection,” IEEE Access, Vol.8, pp. 114900-114909, 2020.
  14. [14] P. Barmpoutis, P. Papaioannou, K. Dimitropoulos, and N. Grammalidis, “A review on early forest fire detection systems using optical remote sensing,” Sensors, Vol.20, No.22, 6442, 2020.
  15. [15] A. E. Çetin, K. Dimitropoulos, B. Gouverneur, N. Grammalidis, O. Günay, Y. H. Habiboğlu, B. U. Töreyin, and S. Verstockt, “Video fire detection – review,” Digital Signal Processing, Vol.23, No.6, pp. 1827-1843, 2013.
  16. [16] Y. Lee, D. Im, and J. Shim, “Data labeling research for deep learning based Fire Detection System,” Proc. of the 2019 Int. Conf. on Systems of Collaboration Big Data, Internet of Things & Security (SysCoBIoTS), pp. 1-4, 2019.
  17. [17] S. Frizzi, R. Kaabi, M. Bouchouicha, J.-M. Ginoux, E. Moreau, and F. Fnaiech, “Convolutional Neural Network for video fire and Smoke Detection,” Proc. of the 42nd Annual Conf. of the IEEE Industrial Electronics Society (IECON 2016), pp. 877-882, 2016.
  18. [18] M. J. Park and B. C. Ko, “Two-step real-time night-time fire detection in an urban environment using static ELASTIC-YOLOV3 and temporal fire-tube,” Sensors, Vol.20, No.8, 2202, 2020.
  19. [19] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” Proc. of the 2015 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 1-9, 2015.
  20. [20] W. Wang, Y. Li, T. Zou, X. Wang, J. You, and Y. Luo, “A novel image classification approach via dense-mobilenet models,” Mobile Information Systems, Vol.2020, pp. 1-8, 2020.
  21. [21] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. of the 2016 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, 2016.
  22. [22] M. Pak and S. Kim, “A review of deep learning in image recognition,” Proc. of the 2017 4th Int. Conf. on Computer Applications and Information Processing Technology (CAIPT), pp. 1-3, 2017.

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

Last updated on Dec. 01, 2022