Indian Traffic Signs Recognition | DEEP LEARNING Multi-Model System

Project Overview

The Indian Traffic Signs Recognition System is an AI-powered computer vision solution that uses three deep learning models (InceptionV3, MobileNetV2, DenseNet121) for real-time traffic sign classification. Built with Django web framework, this system provides accurate traffic sign identification to enhance road safety and support intelligent transportation systems.

Key Achievements

• Multi-Model AI: Implements InceptionV3, MobileNetV2, and DenseNet121 for robust classification
• High Accuracy: InceptionV3 achieves 81.68% validation accuracy on Indian traffic sign dataset
• Web-Based Interface: Django-powered responsive web application with user and admin dashboards
• Real-Time Processing: Instant image upload and classification with confidence scoring
• Comprehensive Analytics: Detailed model comparison and performance metrics

🎥 Project Explanation Video

Video Highlights

• Live traffic sign recognition demo with multiple AI models
• Real-time image upload and classification process
• Model performance comparison and accuracy analysis

Technical Stack

Web Framework & Frontend

• Django 4.1+ (Python Web Framework)
• Django Templates (Server-side rendering)
• HTML5 / CSS3 / JavaScript
• Bootstrap 5 (Responsive UI framework)
• Font Awesome (Icon library)

AI & Machine Learning

• TensorFlow/Keras (Deep learning framework)
• InceptionV3 (Convolutional Neural Network)
• MobileNetV2 (Lightweight CNN architecture)
• DenseNet121 (Dense connectivity network)
• OpenCV (Computer vision library)
• NumPy & Pandas (Data processing)
• Pillow (Image processing)

Database & Deployment

• SQLite (Development database)
• Django ORM (Object-relational mapping)
• File-based model storage (.h5 files)

📂 Project Architecture: Indian Traffic Signs Recognition System

Indian_Traffic_Signs_Prediction/
│
├── manage.py                          # Django management script
├── requirements.txt                   # Python dependencies
├── db.sqlite3                        # SQLite database
│
├── assets/                           # Static files and templates
│   ├── static/
│   │   ├── user/                     # User interface assets
│   │   │   ├── img/                  # Images and model files
│   │   │   ├── css/                  # Stylesheets
│   │   │   └── js/                   # JavaScript files
│   │   └── admin/                    # Admin interface assets
│   └── templates/
│       ├── main/                     # Main page templates
│       ├── user/                     # User dashboard templates
│       ├── admin/                    # Admin panel templates
│       └── base-user.html            # Base template
│
├── mainapp/                          # Main Django application
│   ├── views.py                      # Main application logic
│   ├── models.py                     # Database models
│   ├── urls.py                       # URL routing
│   └── migrations/                   # Database migrations
│
├── userapp/                          # User dashboard application
│   ├── views.py                      # User dashboard logic
│   ├── models.py                     # User models
│   └── migrations/                   # User app migrations
│
├── adminapp/                         # Admin panel application
│   ├── views.py                      # Admin panel logic
│   ├── models.py                     # Admin models
│   └── migrations/                   # Admin app migrations
│
├── media/                            # User uploaded files
│   └── uploads/                      # Uploaded images
│
├── models/                           # Trained AI models
│   ├── inception_model.h5            # InceptionV3 trained model
│   ├── mobilnet_model.h5            # MobileNetV2 trained model
│   └── densnet_model.h5             # DenseNet121 trained model
│
└── datasets/                         # Training datasets
    └── indian_traffic_signs/         # Traffic sign images

How It Works:

• Image Upload: Users upload traffic sign images through Django web interface
• Model Selection: System allows choice between InceptionV3, MobileNetV2, or DenseNet121
• Image Preprocessing: Images are resized to 224x224 pixels and normalized
• AI Processing: Selected neural network processes the image for classification
• Result Generation: System provides traffic sign classification with confidence scores
• Dashboard Display: Results are displayed in user-friendly interface with detailed analytics

✨ Core Features

• Multi-Model AI Architecture: Three different CNN architectures for robust classification
• Real-Time Image Processing: Instant upload and classification with immediate results
• User Dashboard: Personalized interface for tracking prediction history
• Admin Analytics Panel: Comprehensive model performance monitoring and comparison
• Responsive Web Design: Mobile-friendly interface accessible on all devices
• Detailed Results: Traffic sign information including rules, penalties, and safety tips

AI Model Performance & Architecture

• InceptionV3: 81.68% validation accuracy - Best performing model with complex architecture
• DenseNet121: 80.98% validation accuracy - Dense connectivity for feature reuse
• MobileNetV2: 77.33% validation accuracy - Lightweight architecture for mobile deployment
• Dataset Scale: 4,438 training images, 1,288 validation images, 1,288 testing images
• Model Storage: Pre-trained models stored as .h5 files for efficient loading

Why This Matters

• Traffic sign recognition is fundamental for autonomous vehicles and advanced driver assistance systems. This multi-model AI solution enhances road safety by accurately identifying Indian traffic signs through three different neural network architectures, supporting smart transportation initiatives and reducing accidents caused by sign misinterpretation.

About:

• Developed by: Praveen Kumar
• For more information, contact: praveen11x@gmail.com
• Location: South Delhi, New Delhi, India
• Phone: +91 9955109280

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