Traffic Sign Recognition System

This project is a state-of-the-art computer vision application designed to automatically recognize and classify traffic signs from images or video streams. Built using deep learning techniques (Convolutional Neural Networks), it achieves high accuracy on the GTSRB (German Traffic Sign Recognition Benchmark) dataset.

Key Features

• Real-time Detection: Capable of processing video feeds for real-time sign recognition.
• High Accuracy: Achieves over 98% accuracy on test datasets using optimized CNN architecture.
• Robust Preprocessing: Includes image enhancement, normalization, and augmentation pipelines to handle varying lighting conditions.
• User Interface: Simple CLI/GUI for testing individual images.

Technology Stack

• Language: Python 3.9+
• Deep Learning: PyTorch / TensorFlow (Configurable)
• Computer Vision: OpenCV
• Data Handling: NumPy, Pandas

Installation

1. Clone the repository.
2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the training script or use the pre-trained model:

   python main.py --predict sample_image.jpg
   

Dataset

This model is trained on the GTSRB dataset, which contains 43 classes of traffic signs.