Intelligent Methods for Forest Fire Detection Using Unmanned Aerial Vehicles

Author:

Abramov Nikolay1ORCID,Emelyanova Yulia1ORCID,Fralenko Vitaly1ORCID,Khachumov Vyacheslav1234,Khachumov Mikhail1234,Shustova Maria1,Talalaev Alexander1

Affiliation:

1. Ailamazyan Program Systems Institute of Russian Academy of Sciences, 152021 Pereslavl-Zalessky, Russia

2. Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, 119333 Moscow, Russia

3. The Ministry of Education and Science, Russia RUDN University, 117198 Moscow, Russia

4. The Ministry of Education and Science, Russia MIREA-Russian Technological University, 119454 Moscow, Russia

Abstract

This research addresses the problem of early detection of smoke and open fire on the observed territory by unmanned aerial vehicles. We solve the tasks of improving the quality of incoming video data by removing motion blur and stabilizing the video stream; detecting the horizon line in the frame; and identifying fires using semantic segmentation with Euclidean–Mahalanobis distance and the modified convolutional neural network YOLO. The proposed horizon line detection algorithm allows for cutting off unnecessary information such as cloud-covered areas in the frame by calculating local contrast, which is equivalent to the pixel informativeness indicator of the image. Proposed preprocessing methods give a delay of no more than 0.03 s due to the use of a pipeline method for data processing. Experimental results show that the horizon clipping algorithm improves fire and smoke detection accuracy by approximately 11%. The best results with the neural network were achieved with YOLO 5m, which yielded an F1 score of 76.75% combined with a processing speed of 45 frames per second. The obtained results differ from existing analogs by utilizing a comprehensive approach to early fire detection, which includes image enhancement and alternative real-time video processing methods.

Funder

RUSSIAN SCIENCE FOUNDATION

regional budget to organizations in the Yaroslavl region

Publisher

MDPI AG

Reference83 articles.

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5. Islam, A.M., Masud, F.B., Ahmed, M.R., Jafar, A.I., Ullah, J.R., Islam, S., Shatabda, S., and Islam, A.K.M.M. (2023). An Attention-Guided Deep-Learning-Based Network with Bayesian Optimization for Forest Fire Classification and Localization. Forests, 14.

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