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iDrone: IoT-Enabled Unmanned Aerial Vehicles for Detecting Wildfires Using Convolutional Neural Networks

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Abstract

The rise of global temperatures, over the past few decades, has disrupted the usual balance of nature. As a result of increasing temperatures, wildfires have destroyed millions of acres of land, thousands of structures, and homes. The pollution and toxic gases produced by the wildfires are carried out to thousands of miles, thus threatening the lives all around the world. Most wildfires occur due to anthropogenic factors, which cannot be predicted solely based on climate conditions. Henceforth, to detect wildfires before escalating, we propose iDrone, which is a wildfire detection system equipped with an end-to-end CNN image classification model: XtinguishNet, trained on a wildfire imagery dataset to detect the possible flames or smokes in an image. In addition, our approach also acquires the weather data and the intensity of the fire. Contrasting with existing wildfire detection systems, our proposed solution is a fusion of the Internet of Things (IoT) and Deep Learning, aiming to provide a one-stop solution for all the needs required to minimize the damage caused by wildfires. When validated and tested using various benchmark datasets, video surveillance, iDrone acquired a high accuracy of 98.36% with the least computational power.

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Acknowledgements

The authors would like to thank the editors and reviewers. The authors Sibi Chakkaravarthy Sethuraman, Gaurav Reddy Tadkapally and Anitha Subramanian would like to thank Dr. S. V. Kota Reddy, Vice Chancellor, VIT-AP University for his support. A special thanks to Dr. Hari Seetha, Director, AIR Center and the team members of AIR Center, VIT-AP University.

Funding

This study was funded by Center for Excellance in Artificial Intelligence and Robotics (AIR), VIT-AP University.

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Authors and Affiliations

  1. School of Computer Science and Engineering and Center of Excellence in Artificial Intelligence and Robotics (AIR), VIT-AP University, Amaravati, India

    Sibi Chakkaravarthy Sethuraman & Gaurav Reddy Tadkapally

  2. Department of Computer Science and Engineering, University of North Texas, Denton, TX, 76207, USA

    Saraju P. Mohanty

  3. School of Electronics Engineering and Center of Excellence in Artificial Intelligence and Robotics (AIR), VIT-AP University, Amaravati, India

    Anitha Subramanian

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  1. Sibi Chakkaravarthy Sethuraman
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  2. Gaurav Reddy Tadkapally
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  4. Anitha Subramanian
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Correspondence to Sibi Chakkaravarthy Sethuraman.

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Sethuraman, S.C., Tadkapally, G.R., Mohanty, S.P. et al. iDrone: IoT-Enabled Unmanned Aerial Vehicles for Detecting Wildfires Using Convolutional Neural Networks. SN COMPUT. SCI. 3, 242 (2022). https://doi.org/10.1007/s42979-022-01160-7

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