Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
권호 표지

Real-Time Fire Detection Method Using YOLOv8

YOLOv8을 이용한 실시간 화재 검출 방법

  • Tae Hee Lee (Computer Education, Sungkyunkwan University) ;
  • Chun-Su Park (Computer Education, Sungkyunkwan University)
  • 이태희 (성균관대학교 컴퓨터교육과) ;
  • 박천수 (성균관대학교 컴퓨터교육과)
  • Received : 2023.06.02
  • Accepted : 2023.06.21
  • Published : 2023.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Since fires in uncontrolled environments pose serious risks to society and individuals, many researchers have been investigating technologies for early detection of fires that occur in everyday life. Recently, with the development of deep learning vision technology, research on fire detection models using neural network backbones such as Transformer and Convolution Natural Network has been actively conducted. Vision-based fire detection systems can solve many problems with physical sensor-based fire detection systems. This paper proposes a fire detection method using the latest YOLOv8, which improves the existing fire detection method. The proposed method develops a system that detects sparks and smoke from input images by training the Yolov8 model using a universal fire detection dataset. We also demonstrate the superiority of the proposed method through experiments by comparing it with existing methods.

Keywords

References

  1. P. Panagiotis, et al. "A review on early forest fire detection systems using optical remote sensing." Sensors, vol. 20, no. 22, pp. 6442, 2020.
  2. F. Khan, et al. "Recent advances in sensors for fire detection." Sensors, vol. 22, no. 9, pp. 3310, 2022.
  3. D. Venancio, et al. "An automatic fire detection system based on deep convolutional neural networks for lowpower, resource-constrained devices." Neural Computing and Applications, vol. 34, no. 18, pp. 15349-15368, 2022. https://doi.org/10.1007/s00521-022-07467-z
  4. M. Mukhiddinov, et al. "Automatic Fire Detection and Notification System Based on Improved YOLOv4 for the Blind and Visually Impaired." Sensors, vol. 22, no. 9, pp. 3307, 2022.
  5. S. Jha, C. Seo, F. Yang, and G. P. Joshi, "Real time object detection and tracking system for video surveillance system." Multimedia Tools and Applications, vol. 80, no. 3, pp. 3981-3996, 2021. https://doi.org/10.1007/s11042-020-09749-x
  6. P. Vinicius, et al. "A hybrid method for fire detection based on spatial and temporal patterns." Neural Computing and Applications, vol. 35, pp. 9349-9361, 2023.
  7. J. Terven and D. Cordova-Esparza. "A comprehensive review of YOLO: From YOLOv1 to YOLOv8 and beyond." arXiv preprint arXiv:2304.00501, 2023.
  8. P. Jiang, D. Ergu, F. Liu, Y. Cai, and B. Ma. "A Review of Yolo Algorithm Developments." Procedia Computer Science, pp. 1066-1073, 2022.
  9. J. Redmon, and A. Farhadi, "YOLO9000: better, faster, stronger." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7263-7271, 2017.
  10. Ali Farhadi and Joseph Redmon. "Yolov3: An incremental improvement." Computer Vision and Pattern Recognition, vol. 1804, pp. 1-6, 2018.
  11. Bochkovskiy, Alexey, Chien-Yao Wang, and Hong-Yuan Mark Liao. "Yolov4: Optimal speed and accuracy of object detection." arXiv preprint arXiv:2004.10934, 2020.
  12. C. Y. Wang, A. Bochkovskiy, and H. Y. H. Liao. "Scaled-yolov4: Scaling cross stage partial network." In Proceedings of the IEEE/cvf conference on computer vision and pattern recognition. pp. 13029-13038, 2021.
  13. W. Wu, et al. "Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image." PloS one, vol. 16, no. 10, pp. e0259283. 2021.
  14. https://github.com/meituan/YOLOv6
  15. C. Y. Wang, A. Bochkovskiy, and H. Y. M. Liao, "YOLOv7: Trainable bag-of-freebies sets new state-ofthe- art for real-time object detectors." arXiv preprint arXiv:2207.02696.
  16. G. Ang, et al. "A novel application for real-time arrhythmia detection using YOLOv8." arXiv preprint arXiv:2305.16727, 2023.
  17. https://github.com/RangeKing
  18. C. S. Park, "YOLOv7 Model Inference Time Complexity Analysis in Different Computing Environments." Journal of the Semiconductor & Display Technology, vol. 21, no. 3, pp. 7-11, 2022.
  19. C. S. Park, "Performance Analysis of DNN inference using OpenCV Built in CPU and GPU Functions." Journal of the Semiconductor & Display Technology, vol. 21, no. 1, pp. 75-78, 2022.