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A computer vision–powered smart self-checkout system that combines YOLOv11 for real-time product detection and Flask for a seamless, interactive billing interface — designed to revolutionize the retail experience.

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🛒 Smart Trolley - Self Checkout System

A computer vision–powered smart self-checkout system that combines YOLOv11 for real-time product detection and Flask web interface for a seamless, interactive billing interface — designed to revolutionize the retail experience.


🚀 Features

  • 📦 Real-time Object Detection using YOLOv11
  • 🎥 Live Camera Integration for scanning products
  • 🧠 Dynamic Product Recognition (from custom-trained dataset)
  • 🧾 Flask-based Web UI for cart, checkout, and payment simulation
  • 🔍 Product Search with Add-to-Cart and Quantity Control
  • 🌙 Dark/Light Theme Toggle with Tailwind CSS
  • 🔊 Audio Feedback and Toastr notifications
  • 💳 Simulated Payment Form with validation & UX animations

🎯 Tech Stack

Layer Technologies Used
Frontend HTML, CSS, TailwindCSS, JavaScript, jQuery, Toastr.js
Backend Python, Flask
CV & ML YOLOv11 (Ultralytics), OpenCV
Deployment GitHub Pages (Frontend), Flask Local Server
Tools Git, GitHub, Github Pages (UI Hosting) VS Code, Roboflow (for Dataset)

📁 Folder Structure

SmartTrolley - Self Checkout/
├── backend/
│   ├── app.py               # Main Flask app
│   ├── detector.py          # YOLO detector logic
│   └── products.json        # Product catalog with metadata
├── frontend/
│   ├── static/              # JS, CSS, audio files
│   └── templates/           # index.html, checkout.html
├── yolo/                    # YOLO training & inference scripts
├── .gitignore
├── requirements.txt
└── README.md

🌐 Live Demo & Hosted UI

🎥 Demo Video: Watch it on LinkedIn


🔧 Installation Guide/Run Locally

1. Clone the Repository

git clone https://github.com/princearora-4/smart-trolley.git
cd smart-trolley

2. Setup Virtual Environment (Optional but Recommended)

python -m venv venv
venv\Scripts\activate # On Windows
source venv/bin/activate # On Unix/Mac

3. Install Dependencies

pip install -r requirements.txt

4. Run Flask Server

cd backend
python app.py

Visit: http://127.0.0.1:8080/ in any of your browser.


📜 Requirements

Contents of requirements.txt:

flask
opencv-python
ultralytics
numpy
scikit-image
psutil
matplotlib
torch
tqdm
PyYAML
filterpy
lap
requests
scipy

🧠 Object Detection & Dataset

  • Built with YOLOv11 (via Ultralytics)
  • Custom dataset containing grocery items (Maggi, Amul Darkchocolate, Balaji Wafers, etc.)
  • Annotated using Roboflow and trained using YOLO format
  • best.pt weights used in detector.py for live detection via OpenCV

📂 Dataset: Download Dataset (Google Drive)


📌 Core Functional Files

File Description
app.py Main backend Flask app
detector.py Loads YOLO model and performs real-time detection
products.json Product metadata (name, price, description, audio)
index.html Shopping cart UI with camera feed and detection
checkout.html Billing and payment UI with form validation
script.js Handles cart state, AJAX calls, search & UI events

🚀 Deployment Strategy

  • Frontend hosted using GitHub Pages via frontend-pages branch
  • ⚙️ Backend served locally via Flask
  • 🧩 Future Scope: Dockerize app, deploy via Vercel, Railway, or Render

🏆 Highlights

  • ✅ Fully functional, solo-developed project
  • 🎯 Combines Computer Vision, Web Development, and UX seamlessly
  • 🧪 End-to-end tested and hosted using modern DevOps workflows

📬 Contact

Prince Arora 🌐 LinkedIn 🔗 GitHub 📧 princeharora4@gmail.com


📄 License

This project is licensed under the MIT License.

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A computer vision–powered smart self-checkout system that combines YOLOv11 for real-time product detection and Flask for a seamless, interactive billing interface — designed to revolutionize the retail experience.

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