Abstract
Forest fires have a very bad impact on the natural environment and human beings. To protect the environment and enhance human safety, it is important to detect the source of a fire before it spreads. The existing fire detection algorithms have a weak generalization and do not fully consider the influence of fire target size on detection. To enhance the ability of fire detection of different sizes, ground fire data and Unmanned Aerial Vehicle (UAV) forest fire data are combined in this paper. To improve the detection accuracy of the model, a cosine annealing algorithm, label smoothing, and multi-scale training are introduced. The experimental results show that the Improved-YOLOv5s model proposed in this paper has strong generalization and a good detection effect for different sizes of fires. The mean Average Precision (mAP) value reaches 88.7%, 8% higher than that of YOLOv5s mAP. The proposed model has the advantages of strong generalization and high precision.
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References
Muys, B.: Forest Ecosystem Services. Encyclopedia of the UN Sustainable Development Goals (2020)
Holden, S.R., Rogers, B.M., Treseder, K.K., Randerson, J.T.: Fire severity influences the response of soil microbes to a boreal forest fire. Environ. Res. Lett. 11, 035004–035004 (2016). https://doi.org/10.1088/1748-9326/11/3/035004
Matin, M.A., Chitale, V.S., Murthy, M.S.R., Uddin, K., Bajracharya, B., Pradhan, S.: Understanding forest fire patterns and risk in Nepal using remote sensing, geographic information system and historical fire data. Int. J. Wildland Fire 26, 276–286 (2017). https://doi.org/10.1071/wf16056
Bo, P.: Research on Classification of Forest Fire Risk Based on GIS Technology in Xichang City, Sichuan Province (2021)
Premsai, D., Reddy, G.K.J., Gudipalli, A.: Forest fire detection using wireless sensor networks. Int. J. Smart Sens. Intell. Syst. 13, 1–8 (2020). https://doi.org/10.21307/ijssis-2020-006
Noureddine, H., Bouabdellah, K.: Field Experiment Testbed for Forest Fire Detection using Wireless Multimedia Sensor Network (2020). https://doi.org/10.2174/2210327909666190219120432
Varela, N., Díaz-Martinez, J.L., Ospino, A., Zelaya, N.A.L.: Wireless sensor network for forest fire detection. FNC/MobiSPC (2020). https://doi.org/10.1016/j.procs.2020.07.061
Bouakkaz, F., Ali, W., Derdour, M.: Forest fire detection using wireless multimedia sensor networks and image compression. Immunotechnology 20, 57–63 (2021). https://doi.org/10.18280/i2m.200108
Mithira, S., Kavi, S., Ilakiya, S.: Efficient Fire Detection Using Hog Feature Extraction In Machine Learning (2020)
Jin, S., Lu, X.: Vision-based forest fire detection using machine learning. In: Proceedings of the 3rd International Conference on Computer Science and Application Engineering (2019). https://doi.org/10.1145/3331453.3361659
Mishra, R., Gupta, L., Gurbani, N., Shivhare, S.N.: Image-based forest fire detection using bagging of color models. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds.) International Conference on Innovative Computing and Communications. AISC, vol. 1394, pp. 477–486. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-3071-2_38
Wahyono, Harjoko, A., Dharmawan, A., Adhinata, F.D., Kosala, G., Jo, K.-H.: Real-time forest fire detection framework based on artificial intelligence using color probability model and motion feature analysis. Fire (2022). https://doi.org/10.3390/fire5010023
Nazarenko, E., Varkentin, V., Polyakova, T.: Features of application of machine learning methods for classification of network traffic (features, advantages, disadvantages). In: 2019 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon), pp. 1–5 (2019). https://doi.org/10.1109/fareastcon.2019.8934236
Bhoomika, C.H., Rakshitha, B.H.: A survey on machine learning. Int. J. Eng. Appl. Sci. Technol. (2021)
Goodfellow, I.J., Bengio, Y., Courville, A.C.: Deep learning. Nature 521, 436–444 (2015)
Zhang, Q.X., et al.: Wildland forest fire smoke detection based on faster R-CNN using synthetic smoke images. Procedia Eng. 211, 441–446 (2018)
Barmpoutis, P., Dimitropoulos, K., Kaza, K., Grammalidis, N.: Fire detection from images using faster R-CNN and multidimensional texture analysis. In: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8301–8305 (2019)
Saponara, S., Elhanashi, A.E., Gagliardi, A.: Exploiting R-CNN for video smoke/fire sensing in antifire surveillance indoor and outdoor systems for smart cities. In: 2020 IEEE International Conference on Smart Computing (SMARTCOMP). IEEE (2020)
Wu, S., Zhang, L.: Using popular object detection methods for real time forest fire detection. In: 2018 11th International Symposium on Computational Intelligence and Design (ISCID), vol. 1, pp. 280–284 (2018). https://doi.org/10.1109/iscid.2018.00070
Wang, S., et al.: Forest fire detection based on lightweight yolo. In: 2021 33rd Chinese Control and Decision Conference (CCDC), pp. 1560–1565 (2021). https://doi.org/10.1109/ccdc52312.2021.9601362
Xu, R., Lin, H.-X., Kang, L., Cao, L., Liu, Y.: A forest fire detection system based on ensemble learning. Forests 12, 217 (2021). https://doi.org/10.3390/f12020217
Ko, B.C., Ham, S.J., Nam, J.Y.: Modeling and formalization of fuzzy finite automata for detection of irregular fire flames. IEEE Trans. Circuits Syst. Video Technol. 21, 1903–1912 (2011). https://doi.org/10.1109/tcsvt.2011.2157190
Grammalidis, N., Dimitropoulos, K., Cetin, E.: FIRESENSE Database of Videos for Flame and Smoke Detection. Zenodo (2017). https://doi.org/10.5281/zenodo.836749
Dunnings, Andy Fire Image Data Set for Dunnings 2018 Study - PNG Still Image Set. Durham University
Shamsoshoara, A., Afghah, F., Razi, A., Zheng, L., Fulé, P., Blasch, E.: The FLAME dataset: aerial imagery pile burn detection using drones (UAVs). https://doi.org/10.1016/j.comnet.2021.108001
Muhammad, K., Ahmad, J., Mehmood, I., Rho, S., Baik, S.W.: Convolutional neural networks based fire detection in surveillance videos. IEEE Access 6, 18174–18183 (2018). https://doi.org/10.1109/ACCESS.2018.2812835
Chen, Z., Yang, J.-C., Chen, L., Jiao, H.: Garbage classification system based on improved ShuffleNet v2. Resources, Conservation and Recycling (2022)
Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., Sun, J.: RepVGG: making VGG-style ConvNets great again. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13728–13737 (2021)
Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9992–10002 (2021)
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Zhou, M., Li, J., Liu, S. (2022). Fire Detection Based on Improved-YOLOv5s. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13532. Springer, Cham. https://doi.org/10.1007/978-3-031-15937-4_8
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