Overview:

The Traffic Sign Recognition (TSR) project is an AI-based computer vision system designed to automatically detect and classify traffic signs from images or real-time video feeds.
Using Convolutional Neural Networks (CNNs) and Deep Learning algorithms, the system can accurately recognize road signs such as speed limits, stop signs, pedestrian crossings, no entry signs, and more.

This technology plays a crucial role in autonomous vehicles, driver assistance systems, and road safety monitoring, helping machines understand and respond to road signs like a human driver.

Objectives:

• To develop a Deep Learning model capable of classifying different types of traffic signs.

• To enhance road safety by enabling real-time detection of road signs in vehicles.

• To demonstrate the integration of image processing, machine learning, and computer vision.

• To create a smart system that can assist self-driving cars and traffic management systems.

Key Features:

1. Real-Time Traffic Sign Detection: Recognizes signs from live video or uploaded images.

2. AI-Powered Classification: Uses CNNs for accurate and fast recognition.

3. Camera Input Integration: Compatible with webcams, mobile cameras, or video feeds.

4. Pre-Trained Dataset (GTSRB): Trained on the German Traffic Sign Recognition Benchmark dataset.

5. Confidence Score Display: Shows probability for each recognized sign.

6. Custom Model Training: Allows retraining for new or region-specific traffic signs.

7. Image Preprocessing: Uses OpenCV for resizing, normalization, and color adjustments.

8. Performance Visualization: Graphs for model accuracy and loss during training.

9. Multi-Class Classification: Detects multiple types of road signs simultaneously.

10. Web-Based or Mobile Interface: Simple and responsive UI for testing.

Tech Stack:

• Frontend: HTML, CSS, Bootstrap, JavaScript

• Backend: Python (Flask / Django) / Node.js / PHP

• Machine Learning / Deep Learning:

  • Libraries: TensorFlow, Keras, OpenCV, NumPy, Matplotlib, scikit-learn

  • Model Type: Convolutional Neural Network (CNN)

  • Algorithms: Image Classification, Feature Extraction, Softmax Classifier

• Dataset:

  • GTSRB (German Traffic Sign Recognition Benchmark)

  • Optional: Custom datasets with local/regional traffic signs

• Database: MySQL / MongoDB (for saving sign data and logs)

Workflow:

1. Data Collection:

   • Load the GTSRB dataset or collect custom traffic sign images.

2. Data Preprocessing:

   • Resize and normalize images.

   • Apply image augmentation (rotation, contrast, flipping) for better generalization.

3. Model Training:

   • Train a CNN to classify traffic signs into predefined categories.

   • Use training-validation split to ensure model accuracy.

4. Prediction Phase:

   • Feed real-time or uploaded image data into the trained model.

   • Predict and display the detected sign name and confidence percentage.

5. Visualization:

   • Show detection output with bounding boxes or label overlay on images/videos.

Use Case Example:

A car equipped with a dashboard camera streams real-time video to the Traffic Sign Recognition system.
When the camera captures a “Speed Limit 60” sign, the model instantly detects and classifies it.
The system then alerts the driver to adjust speed — or in a self-driving car, automatically limits the speed.

Applications:

• Autonomous Vehicles: Core component for self-driving cars.

• Driver Assistance Systems: Helps human drivers recognize missed or unclear signs.

• Smart City Traffic Monitoring: Can be used in CCTV systems to track sign violations.

• AI and ML Education: Demonstrates real-world computer vision applications.