Enhancing Wildfire & Smoke Detection with Deep Learning: A Fast AI-Based Approach

Wildfires are a significant environmental threat, causing extensive damage to ecosystems, infrastructure, and human life. Traditional detection methods, such as satellite imaging and sensor-based systems, often suffer from delays, high costs, and limited coverage, making early intervention challenging. This project proposes a deep learning-based wildfire, smoke, and fog detection system using FastAI to enable real-time and accurate identification of hazardous conditions. The model is built on a convolutional neural network (CNN) and trained using a labeled dataset containing images of fire, smoke, fog, and non-hazardous scenarios. To improve generalization, data prepro- cessing techniques such as data augmentation and normalization were applied. The model leverages transfer learning to achieve higher accuracy with reduced computational resources, making it feasible for real-world deployment. Performance evaluation was conducted using accuracy, precision, recall, and F1-score for fire, smoke, and fog classification, demonstrating reliable detection capabilities. The system can be integrated with real- time surveillance sources such as CCTV cameras, drones, and satellite feeds, allowing early detection and rapid response to potential wildfire and visibility hazards caused by fog. Compared to conventional methods, this approach offers a cost-effective, scalable, and automated solution for environmental monitoring. By leveraging deep learning and real-time image analysis, the proposed system enhances disaster preparedness and prevention efforts. Future work will focus on expanding the dataset, op- timizing the model for real-time processing, and integrating it with cloud-based deployment for large-scale applications. This study highlights the potential of AI-driven fire, smoke, and fog detection in improving disaster response, visibility monitoring, and wildfire mitigation.