International Journal of Advanced Culture Technology

The International Promotion Agency of Culture Technology (국제문화기술진흥원)
권호 표지
DOI QR코드

DOI QR Code

Design and Implementation of Fire Detection System Using New Model Mixing

  • Gao, Gao (Department of Computer Engineering, Honam University) ;
  • Lee, SangHyun (Department of Computer Engineering, Honam University)
  • Received : 2021.11.30
  • Accepted : 2021.12.07
  • Published : 2021.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we intend to use a new mixed model of YoloV5 and DeepSort. For fire detection, we want to increase the accuracy by automatically extracting the characteristics of the flame in the image from the training data and using it. In addition, the high false alarm rate, which is a problem of fire detection, is to be solved by using this new mixed model. To confirm the results of this paper, we tested indoors and outdoors, respectively. Looking at the indoor test results, the accuracy of YoloV5 was 75% at 253Frame and 77% at 527Frame, and the YoloV5+DeepSort model showed the same accuracy at 75% at 253 frames and 77% at 527 frames. However, it was confirmed that the smoke and fire detection errors that appeared in YoloV5 disappeared. In addition, as a result of outdoor testing, the YoloV5 model had an accuracy of 75% in detecting fire, but an error in detecting a human face as smoke appeared. However, as a result of applying the YoloV5+DeepSort model, it appeared the same as YoloV5 with an accuracy of 75%, but it was confirmed that the false positive phenomenon disappeared.

Keywords

References

  1. K. Ge. F, "2019 Year Fire Statistical Yearbook," 1967.
  2. T. Celik and H. Demirel, "Fire Detection in Video Sequences Using a Generic color Model," Fire Safety Journal, Vol. 44, No. 2, pp. 147-158, Feb. 2009. https://doi.org/10.1016/j.firesaf.2008.05.005
  3. Young-Jin Kim, Eun-Gyung Kim, "Image based Fire Detection using Convolutional Neural Network," Journal of the Korea Institute of Information and Communication Engineering, Vol. 20, No. 9, pp. 1649-1656, 2016. DOI: https://doi.org/10.6109/JKIICE.2016.20.9.1649
  4. T. H. Chen, P. H. Wu, and Y. C. Chiou, "An early fire-detection method based on image processing," Proc. - Int. Conf. Image Process. ICIP, Vol. 3, pp. 1707-1710, 2004. DOI: https://doi.org/10.1109/ICIP.20 04.1421401.
  5. P. Foggia, A. Saggese, and M. Vento, "Real-Time Fire Detection for Video-Surveillance Applications Using a Combination of Experts Based on Color, Shape, and Motion," IEEE Trans. Circuits Syst. Video Technol., Vol. 25, No. 9, pp. 1545-1556, 2015. DOI: https://doi.org/10.1109/TCSVT.2015.2392531.
  6. K. Muhammad, J. Ahmad, I. Mehmood, S. Rho, and S. W. Baik, "Convolutional Neural Networks Based Fire Detection in Surveillance Videos," IEEE Access, Vol. 6, No. c, pp. 18174-18183, 2018. DOI: https://doi.org/10.1109/ACCESS.2018.2812835.
  7. A. Bochkovskiy, C. Y. Wang, and H. Y. M. Liao, "YOLOv4: Optimal Speed and Accuracy of Object Detection," arXiv, 2020. [Online] arXiv:2004.10934
  8. C. Y. Wang, H. Y. Mark Liao, Y. H. Wu, P. Y. Chen, J. W. Hsieh, and I. H. Yeh, "CSPNet: A new backbone that can enhance learning capability of CNN," IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work. Vol. 2020-June, pp. 1571-1580, 2020. DOI: https://doi.org/10.1109/CVPRW50498.202 0.00203.
  9. J. Redmon and A. Farhadi, "YOLOv3: An incremental improvement," arXiv, 2018.
  10. X. Li, T. Lai, S. Wang, Q. Chen, C. Yang, and R. Chen, "Weighted feature pyramid networks for object detection," Proc. - 2019 IEEE Intl Conf Parallel Distrib. Process. with Appl. Big Data Cloud Comput. Sustain. Comput. Commun. Soc. Comput. Networking, ISPA/BDCloud/SustainCom/SocialCom 2019, pp. 1500-1504, 2019. DOI: https://doi.org/10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.002 17.
  11. S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia, "Path Aggregation Network for Instance Segmentation," Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 8759-8768, 2018. DOI : https://doi.org/10.1109/CVPR.2018.00913.