RoadIntel 🚦🚗

A deep learning-powered system for real-time traffic sign classification, engineered for high accuracy and optimized performance in production environments.

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🔗 Quick Access

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✨ Core Features

RoadIntel delivers robust and efficient traffic sign recognition capabilities:

• 🧠 Real-Time Image Classification: Utilizes Convolutional Neural Networks (CNNs) for accurate and rapid identification of traffic signs.
• 🎯 High Accuracy: Engineered and fine-tuned to significantly reduce misclassification rates by 25%, ensuring reliable detection.
• ⚙️ End-to-End ML Pipeline: Designed a complete machine learning pipeline covering data preprocessing, model training, evaluation, and deployment.
• 🚀 Performance Optimized: Optimized model inference performance for production use, identifying and eliminating bottlenecks to process large datasets efficiently.
• 📊 Large Scale Data Handling: Processed over 500+ real-world traffic images for comprehensive model training and validation.

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🛠️ Tech Stack & Architecture

Built with a focus on deep learning frameworks and scientific computing libraries:

• Deep Learning Framework:
  • TensorFlow
• Image Processing & Computer Vision:
  • OpenCV
• Data Manipulation & Scientific Computing:
  • NumPy
  • scikit-learn
• Languages:
  • Python
• Tools & Platforms:
  • Git
  • GitHub

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🚀 Getting Started

Follow these steps to set up and run RoadIntel locally for development and testing.

Prerequisites

Ensure you have the following installed on your system:

• Python: v3.8+
• pip: Python package installer
• Recommended: conda or venv for environment management

Installation

# 1. Clone the repository
git clone [https://github.com/manirht/RoadIntel.git](https://github.com/manirht/RoadIntel.git)

# 2. Navigate into the project directory
cd RoadIntel

# 3. (Optional) Create and activate a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: .\venv\Scripts\activate

# 4. Install project dependencies
pip install -r requirements.txt