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Image Classification Using Parrot FPV Drones

This example shows you how to use the MATLAB® Support Package for Parrot® Drones to classify images captured by the drone's FPV camera.


The MATLAB® Support Package for Parrot® Drones enables you to control the Parrot drone and capture images from the first-person view (FPV) camera. The images captured by the drone's FPV camera can be classified using GoogLeNet, a pretrained deep convolutional neural network. GoogLeNet is trained on more than a million images from ImageNet database. It takes the image as input and provides a label for the object in the image.


Complete Getting Started with MATLAB Support Package for Parrot Drones.

Required Hardware

To run this example, you need:

  • A fully charged Parrot FPV drone

  • A computer with a WiFi connection

Task 1 — Create a Connection to the Parrot Drone

Create a parrot object.

  parrotObj = parrot;

Task 2 — Create the GoogLeNet Neural Network Object

Create a GoogLeNet neural network object.

  nnet = googlenet;

Task 3 — Activate FPV Camera

Start the drone flight and activate the FPV camera.


Create a connection to the drone's FPV camera.

  camObj = camera(parrotObj, 'FPV');

Task 4 — Capture and Classify the Object in the Image

Move the drone forward for 2 seconds along the edges of a square path. Capture the image of an object. Classify the image while the drone moves forward.

1 Move the drone forward for the default duration of 0.5 seconds for each forward step, ensuring a nonblocking behavior. This enables the drone to capture the image and classify it while in motion.

2 Capture a single frame from the drone's FPV camera.

3 Resize the image and classify the object in image using the neural network.

4 Display the image with title as the label returned by the classify function.

5 Turn the drone by π/2 radians at each square vertex.

  tOuter= tic;
  while(toc(tOuter)<=30 && parrotObj.BatteryLevel>20)
     tInner = tic;
     % Keep moving the drone for 2 seconds along each square path edge
        moveforward(parrotObj);                        % Move the drone forward for default time of 0.5 seconds (nonblocking behavior)
        picture = snapshot(camObj);                    % Capture image from drone's FPV camera
        resizedPicture = imresize(picture,[224,224]);  % Resize the picture
        label = classify(nnet,resizedPicture);         % Classify the picture
        imshow(picture);                               % Show the picture
        title(char(label));                            % Show the label
     turn(parrotObj,deg2rad(90));                       % Turn the drone by pi/2 radians

6 Execute steps 1–5 for 30 seconds.

For example, the drone classifies a monitor screen as captured by the FPV camera.

Task 5 — Land the Drone

Land the drone.


Task 6 — Clean Up

When finished, clear the connection to the Parrot drone, the FPV camera, and GoogLeNet.

  clear parrotObj;
  clear camObj;
  clear nnet;