Detecting Wildfire Hazards Using Convolutional Neural Networks

Wildfires are a serious environmental and safety hazard worldwide, requiring precise and timely detection systems. This paper introduces a new wildfire detection system based on deep learning methods to classify CCTV images into fire and non-fire classes with high accuracy. Our method combines Convolutional Neural Networks (CNN) with transfer learning methods and includes meteorological information (temperature, humidity, wind direction) to improve prediction accuracy. The methodology proposed attains <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$95{\text{\% }}$</tex> accuracy with state-of-the-art CNNs and 99% accuracy with transfer learning using the InceptionV3 architecture on domain-specific datasets. This improvement over conventional systems ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$75 - 80{\text{\% }}$</tex> accuracy) allows for quick response to new fires. We validate the system's efficacy through rigorous testing under various environmental conditions and offer a detailed comparison with current techniques. The solution provides real-time monitoring feature essential to handling wildfires, with the capability to potentially minimize environmental losses and lives lost due to premature intervention. This paper adds to the body of knowledge by being able to successfully integrate image recognition with environmental data analysis to produce a stronger and contextaware system for detecting wildfires.