The current fire-detection methods rely primarily on smoke and temperature detection, which are generally performed in the late stage of fire and thus cannot provide a timely reminder in the early stage of fire. The continuous development of artificial intelligence has enabled machine-vision fire detection. This study proposes a convolutional neural network target-detection algorithm, i.e., You Only Look Once version 4 (YOLOv4), to detect small targets. It offers outstanding characteristics and enables scenic-spot monitoring via the video extraction of real-time fire detection using a significant amount of fire data. The diverse fire scenes can provide accurate and timely detection in the early stage of fire, thus providing favorable early warning and alarm function.

1. [1] J. Wen, T. Ying, D. Nguyen, and S. Teo, “Will tourists travel to post-disaster destinations? A case of 2019 Australian bushfires from a Chinese tourists’ perspective,” Tourism Recreation Research, Vol.45, No.3, pp. 420-424, 2020. https://doi.org/10.1080/02508281.2020.1763025
2. [2] H. Yan, “The Analysis of Tourist Satisfaction Integrating the Artistic Intelligence Convolutional Neural Network and Internet of Things Technology,” IEEE Access, Vol.11, pp. 103628-103635, 2023. https://doi.org/10.1109/ACCESS.2023.3317251
3. [3] M. Cheknane, T. Bendouma, and S. S. Boudouh, “Advancing fire detection: Two-stage deep learning with hybrid feature extraction using faster R-CNN approach,” Signal, Image and Video Processing, Vol.18, No.6, pp. 5503-5510, 2024. https://doi.org/10.1007/s11760-024-03250-w
4. [4] A. Jahagirdar, N. Sathe, S. Thorat, and S. Saxena, “Fire detection in nano-satellite imagery using Mask R-CNN,” Int. J. of Signal and Imaging Systems Engineering, Vol.13, No.1, pp. 19-26, 2024. https://doi.org/10.1504/IJSISE.2024.139980
5. [5] S. Li, Y. Wang, C. Feng, D. Zhang, H. Li, W. Huang, and L. Shi, “A thermal imaging flame-detection model for firefighting robot based on YOLOv4-F model,” Fire, Vol.5, No.5, Article No.172, 2022. https://doi.org/10.3390/fire5050172
6. [6] H. Wu, Y. Hu, W. Wang, and J. Xian, “Ship fire detection based on an improved YOLO algorithm with a lightweight convolutional neural network model,” Sensors, Vol.22, No.19, Article No.7420, 2022. https://doi.org/10.3390/s22197420
7. [7] P. Li and W. D. Zhao, “Image fire detection algorithms based on convolutional neural networks,” Case Studies in Thermal Engineering, Vol.19, Article No.100625, 2020. https://doi.org/10.1016/j.csite.2020.100625
8. [8] J. Zhang, Y. Jia, D. Zhu, W. Hu, and Z. Tang, “Study on the situational awareness system of mine fire rescue using faster ross girshick-convolutional neural network,” IEEE Intelligent Systems, Vol.35, No.1, pp. 54-61, 2020. https://doi.org/10.1109/MIS.2019.2943850
9. [9] X. Zheng, F. Chen, L. Lou, P. Cheng, and Y. Huang, “Real-time detection of full-scale forest fire smoke based on deep convolution neural network,” Remote Sensing, Vol.14, No.3, Article No.536, 2022. https://doi.org/10.3390/rs14030536
10. [10] Y. Wang, C. Hua, W. Ding, and R. Wu, “Real-time detection of flame and smoke using an improved YOLOv4 network,” Signal, Image and Video Processing, Vol.16, No.4, pp. 1109-1116, 2022. https://doi.org/10.1007/s11760-021-02060-8
11. [11] R. Fan and M. Pei, “Lightweight forest fire detection based on deep learning,” IEEE 31st Int. Workshop on Machine Learning for Signal Processing, 2021. https://doi.org/10.1109/MLSP52302.2021.9596409
12. [12] S. Kumar, H. Gupta, D. Yadav, I. A. Ansari, and O. P. Verma, “YOLOv4 algorithm for the real-time detection of fire and personal protective equipments at construction sites,” Multimedia Tools and Applications, Vol.81, No.16, pp. 22163-22183, 2022. https://doi.org/10.1007/s11042-021-11280-6
13. [13] J. Huang, X. Wang, H. Wu, S. Liu, X. Yang, and W. Liu, “Detecting potato seed bud eye using lightweight convolutional neural network (CNN),” Trans. of the Chinese Society of Agricultural Engineering, Vol.39, No.9, pp. 172-182, 2023. https://doi.org/10.11975/j.issn.1002-6819.202303035
14. [14] W. Li, Y. Chen, T. Chen, L. Nan, and Y. Du, “A Research and Design of a Reconfigurable CNN Co-processor for Edge Computing,” J. of Electronics & Information Technology, Vol.46, No.4, pp. 1499-1512, 2024. http://doi.org/10.11999/JEIT230509
15. [15] J. Xi, Y. Wang, H. Cai, and X. Chen, “Similarity perception Siamese network for real-time object tracking,” Optoelectronic Imaging and Multimedia Technology VIII, SPIE, pp. 143-155, 2021.