article · International Journal of Advanced Computer Science and Applications
Forest fires present a growing global environmental hazard, causing severe harm to human life and natural resources. Addressing these challenges requires efficient systems capable of rapid fire detection and continuous surveillance. This research explores the integration of unmanned aerial vehicles, or drones, with modern deep learning object recognition models to improve forest fire management. Equipping drones with specialised cameras and sensors delivers a practical and cost-effective approach to real-time aerial monitoring. The study provides an analysis of current deep learning architectures, focusing on Region-based Convolutional Neural Networks and You Only Look Once frameworks, along with their variants, to assess their suitability for detecting fires. Experimental assessments demonstrate promising outcomes across several performance metrics, confirming the utility of combining computer vision models with autonomous aerial platforms for reliable fire observation.
Forest fires are increasing in both frequency and severity, creating urgent risks for ecosystems and human communities. Using sensor-equipped drones paired with automated image recognition enables faster, safer, and more affordable surveillance over wide wooded areas, helping responders spot fires early before major destruction occurs.
This work points towards applications in aerial environmental surveillance, targeted at emergency response agencies, forestry managers, and conservation bodies seeking automated aerial alerts. Combining off-the-shelf drone hardware with models like YOLO and R-CNN represents an applied testing stage, though the abstract does not state field deployment readiness or specific hardware integration steps.
AI-generated from the published abstract. Always read the original work before citing.
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.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.14569/ijacsa.2023.0140342
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
