🧠 YOLO-Based Room & Object Detection

📌 Project Overview

This project uses YOLO (You Only Look Once) or OpenCV to detect objects within a room from video or image input. The goal is to enhance real-time user interaction by recognizing key objects (e.g., chairs, laptops, bottles) and optionally triggering AI-based actions based on the detected objects.

---

🎯 Features

• Real-time object detection using YOLOv5 or OpenCV DNN
• Object labeling with bounding boxes and confidence scores
• Optional voice or chatbot prompts triggered by specific detections
• Automation-ready (e.g., turn on a device when an object is detected)

---

🔧 Setup Instructions

1. Clone the Repository

git clone https://github.com/yourusername/yolo-room-detection.git
cd yolo-room-detection

2. Install Dependencies

Make sure Python 3.8+ is installed on your system.

Install the required Python packages:

pip install -r requirements.txt

3. Download YOLOv5 Model

Download the pre-trained YOLOv5 weights (e.g., yolov5s.pt) from the official YOLOv5 GitHub Repository.

Place the .pt file into the models/ directory of this project:

mkdir -p models
mv yolov5s.pt models/

📌 Future Enhancements

• 🎙️ Voice Command Integration
  Allow users to control the system or respond to detected objects using voice input.

• 🏠 Multi-Room Detection Logic
  Detect and differentiate between multiple rooms based on visual context or metadata.

• 👤 User-Specific Object Personalization
  Train the system to recognize user-specific objects or preferences for a tailored experience.

• 🌐 Home Automation API Integration
  Integrate with platforms like Home Assistant to trigger real-world actions based on object detection.