Video Based Fire Detection Method Using CNN and YOLO Version 4
DOI:
https://doi.org/10.34818/INDOJC.2022.7.2.654Keywords:
CNN, Deep Learning, Fire Detection, Object Detection, YOLOAbstract
Fire detection is one of the technological efforts to prevent fire incidents. This is very important because the damage caused by fires can be minimized by having a fire detector. There are two types of fire detection, namely traditional-based and computer vision-based. Traditional-based fire detection has many shortcomings, one of which requires a close fire distance for activation. Hence, computer vision-based fire detection is made to cover the shortcomings of traditional-based fire detection. Therefore, in this study, we propose a video-based fire detection using a Convolutional Neural Network (CNN) Deep Learning approach supported by You Only Look Once (YOLO) object detection model version four. This study uses a dataset of various fire scenarios in the form of images and videos. The fire detection built in this study has an accuracy of above 90% with an average detection speed of 34.17 Frame Per Second (FPS).
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References
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[3] Che-Bin Liu, & Ahuja, N. (2004). Vision based fire detection. Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. https://doi.org/10.1109/icpr.2004.1333722
[4] Celik, T., Demirel, H., Ozkaramanli, H., & Uyguroglu, M. (n.d.). Fire detection in video sequences using statistical color model. 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings. https://doi.org/10.1109/icassp.2006.1660317
[5] Ko, B. C., Ham, S. J., & Nam, J. Y. (2011). Modeling and formalization of fuzzy finite automata for detection of irregular fire flames. IEEE Transactions on Circuits and Systems for Video Technology, 21(12), 1903–1912. https://doi.org/10.1109/tcsvt.2011.2157190
[6] Kim, B., & Lee, J. (2019). A video-based fire detection using Deep Learning Models. Applied Sciences, 9(14), 2862. https://doi.org/10.3390/app9142862
[7] Shen, D., Chen, X., Nguyen, M., & Yan, W. Q. (2018). Flame detection using Deep Learning. 2018 4th International Conference on Control, Automation and Robotics (ICCAR). https://doi.org/10.1109/iccar.2018.8384711
[8] Wang, T., Shi, L., Yuan, P., Bu, L., & Hou, X. (2017). A new fire detection method based on flame color dispersion and similarity in consecutive frames. 2017 Chinese Automation Congress (CAC). https://doi.org/10.1109/cac.2017.8242754
[9] Dua, M., Kumar, M., Singh Charan, G., & Sagar Ravi, P. (2020). An improved approach for fire detection using Deep Learning Models. 2020 International Conference on Industry 4.0 Technology (I4Tech). https://doi.org/10.1109/i4tech48345.2020.9102697
[10] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
[11] Li, P., & Zhao, W. (2020). Image fire detection algorithms based on Convolutional Neural Networks. Case Studies in Thermal Engineering, 19, 100625. https://doi.org/10.1016/j.csite.2020.100625
[12] Hidayatullah, P. (2021). Buku Sakti Deep Learning Computer Vision Menggunakan Yolo untuk Pemula. Stunning Vision AI Academy.
[13] Vasilev, I., Slater, D., Spacagna, G., Roelants, P., & Zocca, V. (2019). Python deep learning: Exploring deep learning techniques and neural network architectures with pytorch, Keras, and tensorflow. Packt Publishing.
[14] Cao, C., Tan, X., Huang, X., Zhang, Y., & Luo, Z. (2021). Study of flame detection based on improved Yolov4. Journal of Physics: Conference Series, 1952(2), 022016. https://doi.org/10.1088/1742-6596/1952/2/022016
[15] Mukhiddinov, M., Abdusalomov, A. B., & Cho, J. (2022). Automatic fire detection and notification system based on improved Yolov4 for the blind and visually impaired. Sensors, 22(9), 3307. https://doi.org/10.3390/s22093307
[16] Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016). Convolutional Neural Networks for medical image analysis: Full training or fine tuning? IEEE Transactions on Medical Imaging, 35(5), 1299–1312. https://doi.org/10.1109/tmi.2016.2535302
[17] N Prabhu Ram, R Gokul Kannan, V Gowdham, R Arul Vignesh. (2020). Fire Detection Using Cnn Approach. International Journal Of Scientific & Technology Research (IJSTR).
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Published
2022-08-01
How to Cite
Muhammad Salman Farhan, Febryanti Sthevanie, & Kurniawan Nur Ramadhani. (2022). Video Based Fire Detection Method Using CNN and YOLO Version 4. Indonesian Journal on Computing (Indo-JC), 7(2), 65–78. https://doi.org/10.34818/INDOJC.2022.7.2.654
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Computer Science
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