Drones in Forest Fire Mitigation

• Published in: 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) pp. 1 - 7
• Main Authors: Nayak, Vaishnavi Y, Rao, Vaishnavi G, H, Jagruthi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 19-07-2023
• Subjects: alert; Cameras; classification; CNN; Convolutional neural networks; drones; forest fires; Forestry; hazard; Machine learning; object detection; Optical imaging; Real-time systems; Training; Training data; UAV; warnings
• DOI: 10.1109/ICECCME57830.2023.10252566

The proposed system in this paper utilizes drones and Convolutional Neural Networks (CNN) for fire detection. Traditional smoke sensors can be slow and cost-inefficient, making them less suitable for early fire detection. The authors analyze the scope of CNN and related methodologies for detecting fire and propose a novel system that uses optical cameras mounted on drones to detect and identify forest fire threats in real-time. The system also aims to notify interested parties and authorities by providing alerts and important information such as the specific location and environmental conditions. The use of drones equipped with optical cameras is an innovative approach to early fire detection. The ability of drones to capture images and transmit them in real-time enables the detection and identification of forest fires as they occur. The use of CNN allows for the efficient and accurate analysis of the captured images, resulting in a reliable detection system. Additionally, the system can send alerts to authorities and interested parties, allowing for timely and appropriate action to be taken. Overall, the proposed system has the potential to revolutionize early fire detection and response. The use of modern technology such as drones and CNN can greatly improve the efficiency and accuracy of fire detection, ultimately leading to a safer and more secure environment.