Using Computer Vision with Drones for Georeferencing

Brad Dwyer's post explains how to utilize computer vision with drones to detect solar panels in aerial imagery and map their precise GPS locations through a process called georeferencing. The procedure involves training a computer vision model to identify objects in drone footage, then combining this data with drone flight logs to convert pixel coordinates into geographical coordinates. The article highlights the use of Roboflow, an end-to-end computer vision platform, to either find or train a custom model for object detection, emphasizing the importance of georeferencing to associate image data with real-world positions. It also discusses the technical aspects of converting video data into GPS coordinates, including the need for accurate drone telemetry and the application of trigonometry to determine object locations. The process is geared towards improving the accuracy and utility of drone-captured data, allowing for practical applications such as mapping detected objects on a webpage using mapping APIs. Dwyer also provides insights into tuning the model's detection confidence and handling duplicate or false predictions to enhance the reliability of the output. The post invites readers to explore further by experimenting with pre-trained models or creating custom ones, with supporting resources and tools available on GitHub.