Summary
published: 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), Osaka, Japan The escalating impacts of climate change have no-tably increased the frequency and intensity of forest fires, presenting profound challenges to both environmental sustainability and socio-economic stability. Traditional wildfire management strategies, heavily reliant on suppression tactics, are increasingly inadequate due to their high resource demands and limited adaptability to rapid environmental changes. This study introduces an approach to wildfire detection by integrating advanced deep learning algorithms with practical hardware deployment. Utilizing the FLAME (Fire Luminosity Airborne-based Machine learning Evaluation) dataset, we develop a predictive model using lightweight deep learning architectures like MobileNetV2, EfficientNetBO, NASNetMobile, and ResNetV2101, which are optimized for real-time processing and low computational costs. We deployed this model on the cloud and we used an ESP32 microcontroller-based device equipped with a camera and servo motor, designed to autonomously monitor forest areas and provide immediate fire alerts. This integrated system not only aims to enhance early fire detection but also helps wildfire management to be more proactive and efficient. Our results demonstrate the potential of this technology to significantly reduce the time between fire onset and detection, thereby enabling quicker response times and reducing the overall impact of wildfires. Experimental results show that MobileNetV2 resulted in the best-performing model with a test accuracy of 98.1 %, and the device performed up to expectations in hands-on testing scenarios. This research contributes significantly to the fields of environmental monitoring and disaster management, providing a scalable and effective solution to a critical global challenge intensified by climate change. Our work underscores the potential of this technology to reduce the time from fire detection to response, thereby mitigating the impact of wildfires.
Image Gallery
