Forest Fire Detection in Real Time Using Deep Learning
Keywords:
CNN (Convolutional Neural Network), Forest fire, Fire reports, Inception v3, Location tracking, Real-time monitoring, YOLOv5Abstract
Forest ecosystems play a vital role in sustaining biodiversity, regulating the climate, and providing essential resources such as clean air, water, and carbon storage. However, the increasing frequency of forest fires, exacerbated by climate change, deforestation, and human activities, poses a severe threat to these ecosystems. These fires endanger wildlife, destroy vegetation, contribute to air pollution, and put human settlements at risk. Given the devastating consequences, early and accurate detection of forest fires is crucial for effective mitigation and response.
This project integrates advanced deep learning techniques, utilizing Inception v3 and YOLOv5 architectures to enable real-time fire detection and classification. The system is equipped with continuous surveillance, precise GPS-based location tracking, instant fire alert reports, and identification of smoke and flames, ensuring rapid emergency response. Unlike traditional detection methods, which rely on sensors or manual monitoring, this AI-driven approach leverages computer vision to detect fires efficiently through live camera feeds.
The proposed system has significant applications in environmental conservation, disaster management, and sustainable resource planning. By providing timely alerts and accurate fire localization, the system aids in preventing large-scale destruction, reducing economic losses, and ensuring public safety. Additionally, it enhances firefighting efforts by optimizing response strategies and resource allocation. This research contributes to a proactive approach in mitigating wildfire hazards, promoting ecosystem sustainability, and safeguarding both natural and human environments from the catastrophic effects of uncontrolled fires.
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