Project Overview

This project utilizes the ESP32-S3 AI CAM module and EdgeImpulse to recognize apples and oranges. Through this project, you will learn how to train your own model using EdgeImpulse and deploy it on the ESP32-S3 AI CAM module.

• EdgeImpulse Official Website: https://edgeimpulse.com/
• EdgeImpulse Project Link: [https://studio.edgeimpulse.com/public/571380/live

Data Collection

1. Burn the "CameraWebServer" example code to the ESP32-S3 AI CAM module.
2. Open the serial monitor to check the IP address.
3. Access the IP address through a browser on a device within the same local network. Click the “Start” button to view the camera feed.
4. Save images to your computer by clicking the upper-right corner of the video frame. (It is recommended to save images of different objects in separate folders for easier data labeling during training.)

Note: Collect as much image data as possible to improve model accuracy. For this project, around 50 images of apples and oranges were used.

Collected image dataset:

Data Labeling

1. Create a new project in EdgeImpulse.

2. Click “Add existing data” to upload collected images.

3. Select “Upload data” and upload image files. Enter corresponding labels for the images.

4. In “Data acquisition -> Labeling queue”, mark the object of interest in the images and save.

Example: Labeling oranges:

Training the Model

1. Once all data is labeled, navigate to “Impulse design -> Create impulse” to create and save your impulse.
   Processing blocks explanation: EdgeImpulse Documentation
   

2. Go to the “image” page and click “Save parameters”.
   

3. Navigate to the “Generate features” page and click “Generate features” to extract image features.
   

4. Go to “Object detection”, then click “Save & train” to train the model.
   

5. Once training is complete, review model performance. Adjust parameters and retrain if necessary.
   

6. Go to the "Retrain model" page and click "Train model"
   

Deploying the Model

1. In the “Deployment” page:
   • Select “Arduino library” under “DEFAULT DEPLOYMENT.”
   • Choose “TensorFlow Lite” under “MODEL OPTIMIZATIONS.”
   • Click “Build” to download the library.

2. Extract the library into the “Arduino->libraries” folder.
3. Replace the files in “src\edge-impulse-sdk\tensorflow\lite\micro\kernels” with the modified depthwise_conv.cpp and conv.cpp files.
4. Move the edge_camera folder to the library's examples directory.
5. Open the edge_camera example in Arduino IDE. Update the code to include the library .h file and enter your Wi-Fi credentials. Compile and upload the code to the ESP32-S3 AI CAM module.
6. Open the serial monitor to view the IP address and recognition results. Access the camera feed through the IP address in your browser.