Deepforest: A Deep CNN Framework for Early Forest Fire Detection
TL;DR
DeepForest, a deep Convolutional Neural Network (CNN)-based approach to detect early forest fires from optical and thermal images acquired from ground and aerial cameras, is proposed, demonstrating better accuracy, precision and fewer false alarms than traditional approaches.
Abstract
Forest fires are one of the most damaging natural events, affecting natural habitats, animals, human lives and property. Timely detection is critical to minimize environmental and economic damage. Conventional fire detection systems including watchtowers, satellites, and sensor networks suffer from issues such as slow detection, low coverage, and false alarms. In this paper, we propose DeepForest, a deep Convolutional Neural Network (CNN)-based approach to detect early forest fires from optical and thermal images acquired from ground and aerial cameras. The new system detects fire and smoke patterns in a range of environmental conditions, including fog, haze and dim lighting. This approach involves data collection, preprocessing, CNN-based monitoring, temporal filtering and notification for real-time detection. The proposed system demonstrates better accuracy, precision and fewer false alarms than traditional approaches, and is a promising and scalable system for smart wildfire early warning systems.