Image fire detection module for automatic fire extinguishing system with unmanned ground vehicles

Abstract

Especially in responding to large fires, the use of unmanned vehicles can reduce the risk of people getting hurt or encountering situations where they can get hurt. At the same time, the use of unmanned vehicles can increase the efficiency of the intervention. In this direction, one of the most important modules for the unmanned ground vehicles to be used to achieve the desired results is the fire detection module, which will detect the fire and report it to the necessary systems for intervention. In this study, certain deep learning networks were examined for fire detection. These networks are Faster-RCNN, Mask-RCNN, SSD and YOLO. After these networks were trained with the same data sets, they were compared with FPS and mAP data. As a result, it was seen that the YOLO algorithm gave a more positive result than other deep learning networks in terms of both detection and output speed. As a result, YOLO was selected and used as the deep learning network to be used for fire detection.

Keywords

• YOLO
• Image Processing
• Fire Detection

References

1. Pechony, O., & Shindell, D.T. (2010). Driving forces of global wildfires over the past millennium and the forthcoming century. Proceedings of the National Academy of Sciences, 107, 19167 - 19170.
2. Asselin, H., Ali, A.A., Finsinger, W., & Bergeron, Y. (2014). Effect of increased fire activity on global warming in the boreal forest. Environmental Reviews, 22, 206-219.
3. Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28.
4. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).
5. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). Ssd: Single shot multibox detector. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14 (pp. 21-37). Springer International Publishing.
6. He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 2961-2969).
7. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
8. Pincott, J., Tien, P. W., Wei, S., & Calautit, J. K. (2022). Indoor fire detection utilizing computer vision-based strategies. Journal of Building Engineering, 61, 105154.

9. Moumgiakmas, S. S., Samatas, G. G., & Papakostas, G. A. (2021). Computer vision for fire detection on UAVs—From software to hardware. Future Internet, 13(8), 200.
10. de Venâncio, P. V. A., Lisboa, A. C., & Barbosa, A. V. (2022). An automatic fire detection system based on deep convolutional neural networks for low-power, resource-constrained devices. Neural Computing and Applications, 34(18), 15349-15368.
11. Umar, M. M., Silva, L. C. D., Bakar, M. S. A., & Petra, M. I. (2017). State of the art of smoke and fire detection using image processing. International Journal of Signal and Imaging Systems Engineering, 10(1-2), 22-30.
12. Le Maoult, Y., Sentenac, T., Orteu, J. J., & Arcens, J. P. (2007). Fire detection: a new approach based on a low cost CCD camera in the near infrared. Process Safety and Environmental Protection, 85(3), 193-206.
13. Diaconu, B. M. (2023). Recent advances and emerging directions in fire detection systems based on machine learning algorithms. Fire, 6(11), 441.
14. Sierra, D., Montanaro, W., Kuo, L., & Zohuri, B. (2023). Enhancing Fire Detection through CNN and Transfer Learning: A Comprehensive Research Study. Journal of Engineering and Applied Sciences Technology. SRC/JEAST-242, 174(5), 2-6.
15. Ghali, R., & Akhloufi, M. A. (2023). Deep Learning Approaches for Wildland Fires Remote Sensing: Classification, Detection, and Segmentation. Remote Sensing, 15(7), 1821.
16. Chitram, S., Kumar, S., & Thenmalar, S. (2024). Enhancing Fire and Smoke Detection Using Deep Learning Techniques. Engineering Proceedings, 62(1), 7.
17. Khan, T., Khan, Z. A., & Choi, C. (2023). Enhancing real-time fire detection: an effective multi-attention network and a fire benchmark. Neural Computing and Applications, 1-15.