Object Detection with ESP32-AI Thinker and Edge Impulse

In this blog, we will explore how to leverage the ESP32-AI Thinker module with Edge Impulse to create an intelligent object detection system. We will walk through the entire process—from capturing images using an ESP32 web server, training a model using Edge Impulse, deploying the model as an Arduino library, and running real-time object detection on the ESP32 module.

This guide is perfect for developers, IoT enthusiasts, and makers who want to bring AI-powered vision capabilities to embedded systems.🚀

📌 Prerequisites

Hardware Requirements:

1. ESP32-AI Thinker module (ESP32-CAM)
2. USB-TTL module (for flashing firmware)
3. Jumper wires (for connections)
4. Computer (for programming and training)

Software Requirements:

1. Arduino IDE (with ESP32 board support installed)
2. Edge Impulse Studio (free account required)
3. Eloquent Web Server Example for ESP32 (to capture images)

Step 1: Setting Up an ESP32 Web Server for Data Collection

To train a machine learning model effectively, we need a high-quality dataset with multiple images of the target object under various lighting conditions and angles.

✅ Why Use a Web Server?

Instead of manually transferring images, we will create a local web server on the ESP32 module. This will allow us to capture images directly from a browser and store them on a local machine for dataset creation.

🔧 Implementation:

• Set up an ESP32 Web Server using an eloquent example from the ESP32-CAM library.
• Connect to the ESP32’s WiFi hotspot and access the camera feed via a browser.
• Capture multiple images by clicking a button on the web interface.
• Download the images and store them on your PC for further processing.

👉 Ensure you capture images with different backgrounds, lighting conditions, and orientations to improve model accuracy.

Step 2: Upload the Dataset to Edge Impulse for Model Training

Once we have collected a diverse dataset, we will upload it to Edge Impulse for processing.

Steps:

1. Sign up/Login to Edge Impulse Studio.
2. Create a new project and select ESP32-AI Thinker as the target device.
3. Upload the dataset (captured images) to Edge Impulse.
4. Label the images appropriately (e.g., "Object Detected", "No Object").
5. Train the model and benchmark its performance.

🚀 Edge Impulse will optimize the dataset, extract features, and train a model for real-time object detection.

Step 3: Deploy the Trained Model to ESP32 (Arduino Library)

Once the model is trained and validated, we need to deploy it to the ESP32 module. Edge Impulse provides a precompiled Arduino library that can be directly used in your ESP32 sketch.

Deployment Process:

1. Go to the Deployment section in Edge Impulse.
2. Select Arduino Library as the export format.
3. Download the .zip file containing the trained model.
4. Extract and add the library to Arduino IDE.
5. Include the Edge Impulse library in your ESP32 sketch.

Step 4: Flash the Model onto ESP32 and Test Object Detection

🔥 Time to See AI in Action!

1. Open Arduino IDE and select the ESP32-CAM board.
2. Modify the ESP32 sketch to initialize the camera and run inference using the Edge Impulse model.
3. Compile and upload the firmware to the ESP32 module.
4. Open the Serial Monitor and check the real-time object detection output.

🎯 The ESP32 will now process live images, detect objects, and display accuracy levels in the serial monitor.

🎯 Conclusion & Future Enhancements

By following this workflow, we have successfully implemented AI-powered object detection on the ESP32-AI Thinker module using Edge Impulse. This method enables embedded systems to leverage computer vision for applications such as:

✅ Smart home automation
✅ Security & surveillance
✅ Robotics & AI-powered assistants
✅ Industrial quality control