FOREST FIRE DETECTION USING CONVOLUTIONAL NEURAL NETWORKS

Abstract

Forest fires are one of the most devastating natural disasters, causing severe damage to the environment, wildlife, and human life. Early detection of forest fires is crucial to minimize their destructive impact and facilitate rapid response. This project presents a Convolutional Neural Network (CNN)-based approach for automatic forest fire detection using image data. The system utilizes deep learning techniques to analyze images captured from drones, satellites, or surveillance cameras and accurately identify fire and smoke patterns. The CNN model is trained on a large data set of fire and non-fire images to enhance its ability to distinguish subtle visual cues. The proposed method improves detection accuracy and reduces false alarms compared to traditional machine learning or manual observation techniques. The implementation demonstrates the potential of deep learning in providing a real-time, efficient, and reliable forest fire monitoring system, contributing to environmental protection and disaster management efforts.