A Deep Learning Approach to Forest Fire Detection and Monitoring

Forest fires pose a significant threat to sensitive ecosystems worldwide. Early detection and monitoring of these fires are critical for improving response times and aiding firefighting and evacuation efforts. This research paper proposes an approach that utilizes Convolutional neural network (CNN) based transfer learning models in conjunction with drone-derived images to accurately detect and differentiate between fire and nonfire images. This automated system enhances the efficiency of forest patrols and rapid-fire detection without human intervention. Pre-trained CNNs, including MobileNetV2, ResNet50, Xception, and VGG16, were fine-tuned using a dataset containing forest fire and nonfire images. A sigmoid classifier in the classification layer predicts the probability of an image being a fire or nonfire. Experimental results demonstrate the models' ability to identify fires across various scenarios with competitive processing times, making them highly deployable in forest settings.