Investigation of Combining Deep Learning Object Recognition with Drones for Forest Fire Detection and Monitoring
Mimoun Yandouzi, Mounir GRARI, Mohammed Berrahal, Idriss Idrissi, Omar Moussaoui, Mostafa AZIZI, Kamal Ghoumid, Aissa Kerkour Elmiad
Abstract
Forest fires are a global environmental problem that can cause significant damage to natural resources and human lives. The increasing frequency and severity of forest fires have resulted in substantial losses of natural resources. To mitigate this, an effective fire detection and monitoring system is crucial. This work aims to explore and review the current advancement in the field of forest fire detection and monitoring using both drones or unmanned aerial vehicles (UAVs), and deep learning techniques. The utilization of drones fully equipped with specific sensors and cameras provides a cost-effective and efficient solution for real-time monitoring and early fire detection. In this paper, we conduct a comprehensive analysis of the latest developments in deep learning object detection, such as YOLO (You Only Look Once), R-CNN (Region-based Convolutional Neural Network), and their variants, with a focus on their potential application in the field of forest fire monitoring. The performed experiments show promising results in multiple metrics, making it a valuable tool for fire detection and monitoring.