Forest Fire Detection Using IoT Enabled UAV And Computer Vision

Abstract

Owing to their rapid response capabilities, extended range and improved personnel safety, drones equipped with sensors for forest fire monitoring can process the information through sensors and IoT application. Despite this, existing drone-based forest fire detection systems still present many practical problems for their use in operational conditions. In particular, successful detection of forest fires remains difficult, given the very complicated and unstructured forest environments, the movement of UAV-mounted cameras. These negative effects can seriously cause false alarms or faulty detection. In order to perform this mission, meet the corresponding performance criteria and overcome these increasing challenges, it is essential to investigate ways to increase the probability of successful detection and improve the adaptation capabilities to various circumstances in order to improve the accuracy of the forest fire detection system. Based on the above requirements, this paper focuses on the development of reliable and accurate forest fire detection algorithms applicable to drones. For that purpose, many CNN architectures were trained and compared to detect fire. Obtained results demonstrate the accuracy of the developed system compared to traditional detection algorithms.