🎯 YOLOv8 Object Detection with ESP32-CAM Streaming

A real-time object detection system that integrates an ESP32-CAM and YOLOv8 to identify objects from a live camera feed over WiFi. Detects people, animals, vehicles, and more using a pre-trained COCO model.

---

📸 Project Highlights

• 🧠 YOLOv8 Detection: Fast and accurate object detection using the latest YOLOv8 model.
• 📷 ESP32-CAM Integration: Stream camera images over WiFi directly to your Python application.
• 🎛️ Multiple Resolutions: Choose between high, medium, and low quality streaming.
• 🧰 Lightweight Setup: Python script + low-cost ESP32-CAM module = portable detection system.

---

🛠️ Requirements

Python (PC-Side)

• Python 3.x
• Required packages:

  pip install opencv-python numpy ultralytics

ESP32-CAM

• ESP32-CAM module
• Arduino IDE with ESP32 support
• Required Arduino libraries:
  • esp32cam
  • WebServer

---

⚙️ Setup Guide

🔧 ESP32-CAM

1. Open Arduino IDE and install libraries:

   • Go to Sketch > Include Library > Manage Libraries
   • Search for and install esp32cam, WebServer

2. Configure and Upload Code:

   • Add your WiFi credentials in the ESP32 code:

     const char* ssid = "YOUR_SSID";
     const char* password = "YOUR_PASSWORD";

   • Upload code to ESP32-CAM via FTDI programmer.

3. Get IP Address:

   • Open Serial Monitor after upload to view assigned IP address.
   • Example: http://192.168.1.100/cam-hi.jpg

---

🐍 Python Script

1. Set the ESP32-CAM URL in your script:

   url = "http://<ESP32-IP>/cam-hi.jpg"

2. Run the Python script:

   python script.py

3. View Results:

   • A window will display images with real-time object detection.
   • Press q to exit.

---

🔍 Object Detection Details

• Model: YOLOv8 pre-trained on the COCO dataset
• Classes: 80 object types including:
  • People: 🧍‍♂️🧍‍♀️
  • Animals: 🐶 🐱 🐦
  • Vehicles: 🚗 🚌 🏍️
  • Daily Items: 🪑 📦 🍎 📚

---

🧾 Code Overview

Python Script

• ultralytics.YOLO loads the model
• cv2.VideoCapture or requests.get() fetches image from ESP32
• Object detection runs per frame
• Results shown using OpenCV

ESP32-CAM Arduino Code

• Sets up camera with multiple JPEG endpoints:
  • /cam-lo.jpg, /cam-hi.jpg, etc.
• Starts web server for image access

---

📂 Project Structure

YOLO-ESP32-Detection/
├── script.py             # Python script for detection
├── esp32cam_code.ino     # Arduino sketch
├── README.md             # Project documentation
├── static/               # Optional saved images
└── requirements.txt      # Python dependencies

---

📜 License

This project is licensed under the MIT License.
Use freely for learning, experimentation, or improvement!

---

🤝 Contributing

Contributions are welcome!
Feel free to:

• Submit pull requests
• Report bugs
• Suggest new features

---

📷 Preview

---

Created with ❤️ using OpenCV, Ultralytics, and the awesome ESP32-CAM board.