Aerial Fire Detection with Drone Imagery and Computer Vision

Computer vision technology, particularly using aerial imagery via drones, is enhancing wildfire detection by offering speed and coverage that traditional ground-based equipment or satellite imaging lacks. This approach involves deploying drones equipped with WiFi camera modules to capture images over vast forest areas, which are then analyzed by a computer vision model for fire indicators like flames, smoke, or temperature changes. Key components of the system include data preparation using the FLAME dataset, labeling with Roboflow, and training a YOLOv8 object detection model in a Google Colab Notebook. The trained model, achieving high recall and mean Average Precision (mAP) scores, is deployed back to Roboflow for inference, allowing the detection of fire in both images and video streams. By integrating a tracking framework like BYTETrack, this system improves detection consistency and enables real-time fire alerts to a Fire Control Center, assisting response teams in prompt action to mitigate wildfire risks.