Building a Drone Detection System with YOLO and Python

A practical look at my current work in progress

I am working on an application that uses artificial intelligence to identify drones in video streams. The idea is simple. A camera sends images to a server and that server analyses everything it sees in real time. If a drone enters the frame, the system should recognise it immediately and report the result back to the client.

The application already exists in its basic form, but the recognition model still needs proper training. Instead of relying on generic datasets, I want to train the model with data collected from flights with my own drone. This gives me full control over the situations the model learns from and ensures that the final result fits the conditions in which I want to use it.

Why YOLO is at the core of the system

For this project I chose YOLO because it processes images very quickly. Drone detection is not forgiving. A drone can appear for only a moment, move in unexpected directions or blend into a background of trees or buildings. YOLO is fast enough to keep up while still providing reliable bounding results.

Another reason for using YOLO is the strong support it has within the Python ecosystem. Since my server runs entirely on Python, this combination allows me to experiment, train and deploy without switching tools or frameworks.

Creating my own dataset with my drone

To train the model, I need many examples of what a drone looks like in different circumstances. The best way to gather this material is to record my own flights. I plan to film the drone from multiple distances and from a variety of angles. I will record flights on clear days, cloudy days and during low light moments to capture as much variety as possible.

After collecting the raw footage, I divide the videos into individual frames. Each frame must be annotated so that the model knows where the drone is located. This annotation step is slow and repetitive, but it is essential. Good annotations produce good training data and good training data produces a reliable model.

As I continue recording new flights, the dataset will gradually grow. With each expansion, the model will be able to handle more realistic situations, such as partial visibility, disturbance from background motion or very small appearances of the drone far from the camera.

Training the model and improving accuracy

Once the dataset is large enough, I start the training process. During training YOLO studies each annotated frame and learns which visual patterns belong to the drone. Over time it becomes better at separating the drone from the background and better at ignoring objects that look similar, such as birds or flying debris.

Training is an iterative process. First I train a basic version of the model. I then test it on footage that the model has never seen before. When I find mistakes, I add more examples of that situation to the dataset. This cycle continues until the model performs consistently and no longer struggles with common edge cases.

The Python server that drives the detection

The server that handles the detection is written in Python. It receives frames from the client, processes them through the YOLO model and returns the results instantly. Keeping the heavy computation on the server allows the client application to remain lightweight.

This structure also makes the system more flexible. I can improve or retrain the model without making changes to the client. New features, updated models and performance improvements can be applied entirely on the server side.

Releasing the application once it is production ready

My plan is to release the application publicly once the model reaches a stable and reliable level of accuracy. Before that happens I will continue refining the dataset, improving the server performance and validating the system in multiple environments.

When the project is complete, the application should be able to recognise drones in real time on ordinary camera hardware, with minimal setup and without the need for specialised equipment.