Edge Computing-Based Wildfire Detection and Optimization Algorithm

With the increasing threat posed by wildfires to human life, property, and the environment, wildfire detection has become a crucial task for preventing and minimizing fire-related losses. Traditional wildfire detection often relies on features manually designed to describe fires in images, resulting in insufficient robustness in detection performance. An edge computing-based optimization algorithm for wildfire detection is proposed in this paper to achieve fast and accurate early detection of wildfires. Deep learning and computer vision techniques are combined, and algorithms for fire point detection and classification are designed to identify potential wildfire hotspots. An abnormal event detection and prediction algorithm is introduced to identify abnormal behavior outside of wildfire signs and predict potential trends in wildfire development. The algorithm is validated on wildfire datasets, and experimental results demonstrate improved accuracy and real-time performance in early wildfire detection.