Computer Vision-Based Wildfire Smoke Detection Using UAVs

Ehab Ur Rahman,M Irfan Uddin,Insaf Ullah,Hafiz Ishfaq Ahmad,Muhammad Asghar Khan,Fahad Algarni,Yihong Zhang,Dr Dilbag Singh

doi:10.1155/2021/9977939

Abstract

This paper presents a new methodology based on texture and color for the detection and monitoring of different sources of forest fire smoke using unmanned aerial vehicles (UAVs). A novel dataset has been gathered comprised of thin smoke and dense smoke generated from the dry leaves on the floor of the forest, which is a source of igniting forest fires. A classification task has been done by training a feature extractor to check the feasibility of the proposed dataset. A meta-architecture is trained above the feature extractor to check the dataset viability for smoke detection and tracking. Results have been obtained by implementing the proposed methodology on forest fire smoke images, smoke videos taken on a stand by the camera, and real-time UAV footages. A microaverage F1-score of 0.865 has been achieved with different test videos. An F1-score of 0.870 has been achieved on real UAV footage of wildfire smoke. The structural similarity index has been used to show some of the difficulties encountered in smoke detection, along with examples.

Highlights

Wildfire is a colossal threat to damaging the human and wildlife ecosystem
We present a dataset, grouping several images from different sources such as thin, dense with different color, and texture smoke images, taken from different scenarios such as wildfire and other emergency conditions such as building fires and fires from an explosion. e single shot detectors (SSDs) Inception-V2 state-of-the-art models are trained, and their different parameters such as dropout, batch normalization, and learning rate are tuned to choose the best model for real-time fire detection in videos
It is observed that Video 2, i.e., a dense smoke video has the highest F-score among all that is because of the rich texture, shape, and color. e lowest F-score is observed from thin video samples and that is because of light color and features captured by the camera, but still, the model achieves an accuracy of 64% and an F-score of 0.747, proving the feasibility of the dataset on thin smoke

Summary

Introduction

Wildfire is a colossal threat to damaging the human and wildlife ecosystem. Statistics show that wildfires in Northern California in the United States caused more than 40 deaths and about 50 missing individuals in 2015 [1,2,3]. ere were some major wildfire outbreaks in several countries around the world in the year 2019. A forest fire recently caused 89 fatalities in Australia and burned 3500 homes It became of such incidents of great importance to detect wildfires accurately in advance when it turns into chaos. Traditional methods of wildfire detection, which are mainly based on human observation from watchtowers, are inefficient. Implementing deep learning and computer vision techniques in the application for wildfire smoke detection is scarce. E SSD Inception-V2 state-of-the-art models are trained, and their different parameters such as dropout, batch normalization, and learning rate are tuned to choose the best model for real-time fire detection in videos. Comparisons of the results are obtained on several wildfire smoke videos taken by a UAV and smoke images with different kinds of backgrounds and lighting conditions.

Material and Methods

Training and Detection

Research Findings and Discussion

Results

Conclusion