ECOGUARD: Forest Fire Detection System using Deep Learning Improved with Elastic Weight Consolidation

Forest fires are a serious hazard to both human safety and the environment, necessitating the development of effective detection systems. This research proposes an AI-based forest fire detection system that leverages computer vision and deep learning techniques. The system processes real-time images from cameras installed in forested areas to identify signs of fire or smoke, aiding in forest conservation efforts. By employing pre-trained CNN models, namely ResNet and DenseNet, and incorporating Elastic Weight Consolidation, the system achieves high accuracy while preserving knowledge from prior tasks. The system's ability to learn continuously allows it to adjust and perform better over time without losing important data. The proposed forest fire detection system offers real-time fire detection capabilities, facilitating timely intervention and efficient management of forest fires. Additionally, it boasts cost-effectiveness and multilingual support, making it practical and applicable in diverse forested regions. Overall, this research presents a significant advancement in forest fire detection technology, promising substantial contributions to environmental preservation and community safety. The fusion of AI, computer vision, and continual learning through Elastic Weight Consolidation holds great potential for revolutionizing forest fire management and safeguarding our natural ecosystems.