How to do Object Detection using ESP32-CAM and Edge Impulse YOLO Model

Introduction

In this tutorial, we will guide you through the process of creating an image recognition system using the ESP32-CAM module and the Edge Impulse platform. This project focuses on detecting vegetables such as tomatoes, potatoes, and onions. You'll learn about edge computing, data acquisition, model training, and deployment, with results displayed on an OLED screen.

Step 1: Gather Required Components

To get started, gather the following components:

• ESP32-CAM module
• OLED display (compatible with ESP32)
• Jumper wires
• Breadboard (optional, for easier connections)
• Computer with internet access

Step 2: Set Up the Development Environment

1. Install Arduino IDE:

   • Download and install the Arduino IDE from the official website.

2. Add ESP32 Board Support:

   • Open Arduino IDE.
   • Go to File -> Preferences.
   • Add the following URL to the "Additional Board Manager URLs":

     https://dl.espressif.com/dl/package_esp32_index.json
     

   • Go to Tools -> Board -> Board Manager, search for "ESP32" and install it.

3. Install Required Libraries:

   • Open the Library Manager (Sketch -> Include Library -> Manage Libraries).
   • Install libraries for the OLED display and any other necessary libraries for ESP32-CAM.

Step 3: Capture Data for Model Training

1. Set Up Edge Impulse:

   • Create an account on the Edge Impulse platform.
   • Start a new project for object detection.

2. Capture Images:

   • Use the ESP32-CAM to capture images of the vegetables you want to recognize.
   • Ensure you have diverse images in different lighting conditions and angles.
   • Upload the images to your Edge Impulse project.

Step 4: Train the Machine Learning Model

1. Create a Data Acquisition Pipeline:

   • Use the Edge Impulse interface to create a data acquisition pipeline.
   • Label your images according to the vegetables they represent.

2. Train the Model:

   • Follow the Edge Impulse prompts to train your machine learning model.
   • Choose the YOLO (You Only Look Once) model for real-time object detection.
   • Once training is complete, test the model for accuracy.

Step 5: Deploy the Model to ESP32-CAM

1. Download the Model:

   • After successful training, navigate to the deployment section in Edge Impulse.
   • Download the model for the ESP32 platform.

2. Upload the Model to ESP32-CAM:

   • Open Arduino IDE and load the example sketch for ESP32-CAM.
   • Include the Edge Impulse library and add the downloaded model file.
   • Modify the sketch to initialize the OLED display and run the model.

Step 6: Connect ESP32-CAM and OLED Display

1. Wiring:

   • Connect the OLED display to the ESP32-CAM according to the pin configuration.
   • Ensure proper connections to avoid any display issues.

2. Upload the Final Code:

   • Upload the final sketch to the ESP32-CAM.
   • Make sure the code initializes both the camera and the OLED display.

Step 7: Test the Object Detection System

1. Power Up the ESP32-CAM:

   • Connect the ESP32-CAM to a power source.
   • Open the serial monitor in Arduino IDE to view the output.

2. Observe Results:

   • Point the camera at the vegetables and observe the results on the OLED display.
   • Ensure the model correctly identifies the objects in real-time.

Conclusion

You have successfully built an image recognition system using the ESP32-CAM and Edge Impulse. This project demonstrates the capabilities of edge computing and machine learning in object detection applications. Next, consider experimenting with different datasets, enhancing model accuracy, or integrating additional functionalities to expand your project further. Happy coding!