This blog post will guide you through the fascinating process of extracting the location of objects of interest from drone videos. By integrating computer vision, GPS data, and mapping tools, you’ll be able to visualize elements such as solar panels precisely on a map.

Understanding the Process

Imagine you’re a treasure hunter navigating a vast landscape but relying solely on instructions from an ancient map. In this scenario, the drone video is your terrain and the GPS data is your treasure map. Using computer vision as a powerful magnifying glass, you can pinpoint the exact locations of your ‘treasures’—like solar panels—from the aerial footage. Now, let’s delve into how this technology works!

Getting Started

• Try It in Your Browser: The project is deployed to Github Pages here. You can test it with this sample video and flight log.
• If you have your own drone video, follow these instructions to pull your detailed flight log from Airdata.

How to Set It Up Locally

To run the project on your machine:

• Clone the repository.
• Run npm install in the main directory.
• Execute npm run build:dev to start a webpack build with live reload.
• Open a new terminal window and run npx serve dist.
• Access the project through http://localhost:3000 in your browser.

Customization and Training Your Model

One of the project’s strengths is its adaptability!

• To customize the model, change your publishable_key and model ID in the ROBOFLOW_SETTINGS at the top of main.js.
• For more specific applications like pool detection, you can swap the model ID to use the swimming pool computer vision model from Roboflow Universe.
• If pre-trained models don’t meet your needs, you can create and train your own model using Roboflow.

Troubleshooting Tips

If you encounter issues, consider the following solutions:

• Ensure that all dependencies are installed correctly by running npm install again.
• Check that your model ID is valid and corresponds to the model you want to use.
• Verify that your GPS data is accurately matched to the time stamps of the video footage.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

By following this guide, you will be able to effectively detect and map objects of interest from your drone videos. The possibilities are vast—whether it’s monitoring infrastructural developments or tracking wildlife, your creativity is the only limit!

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.