Overview / Usage

The traffic sign recognition project addresses several challenges:

1. Safety Enhancement: Improves road safety by ensuring drivers are aware of and adhere to traffic signs, reducing accidents caused by missed signs.
2. Driver Assistance: Provides real-time information to drivers, aiding their decision-making and promoting adherence to traffic rules.
3. Autonomous Vehicles: Enables self-driving cars to accurately interpret and respond to traffic signs, contributing to safer and more efficient autonomous navigation.
4. Variability Handling: Addresses variations in lighting, weather, and occlusions, ensuring accurate recognition in diverse conditions.
5. Real-Time Processing: Develops algorithms that swiftly detect and classify signs, allowing timely response by vehicles and drivers.
6. Efficient Traffic Flow: Facilitates smoother traffic flow by ensuring timely and informed driver actions at intersections and road changes.

In real-time production, traffic sign recognition is utilized in various ways:

1. Driver Assistance Systems: Integrated into vehicles, these systems interpret road signs, providing drivers with real-time information on speed limits, warnings, and regulatory signs. Alerts and notifications help drivers stay compliant and aware.
2. Autonomous Vehicles: Traffic sign recognition is essential for self-driving cars. It informs the vehicle about speed limits, stop signs, and other traffic rules, enabling safe navigation and decision-making.
3. Traffic Management: Installed at intersections and highways, cameras equipped with traffic sign recognition assist traffic management systems. They gather data on traffic flow and violations, aiding in congestion management and law enforcement.
4. Smart Cities: Traffic sign recognition is a part of creating smart urban environments. It assists with monitoring road conditions, optimizing traffic signal timing, and enhancing overall traffic management.
5. Road Safety Campaigns: Authorities use traffic sign recognition to assess road safety conditions. Insights help identify areas with high compliance issues and plan safety campaigns or road improvements.

Methodology / Approach

Methodology:

1. Problem Understanding:

   • Define the scope: Identify the types of traffic signs to classify.
   • Understand the requirements: Real-time processing, accuracy, and robustness.

2. Data Collection and Preprocessing:

   • Collect a diverse dataset of labeled traffic sign images.
   • Preprocess images using OpenCV: resize, normalize, and augment for better generalization.

3. Model Selection:

   • Choose a suitable model architecture: Convolutional Neural Network (CNN) for image classification.
   • Leverage TensorFlow's Keras for model development due to its ease of use.

4. Model Architecture Design:

   • Design the CNN architecture: convolutional layers for feature extraction, pooling for downsampling, and fully connected layers for classification.

5. Data Splitting:

   • Divide the dataset into training, validation, and test sets.
   • Maintain class balance to prevent bias.

6. Model Training:

   • Train the CNN model on the training set using TensorFlow/Keras.
   • Implement techniques like dropout and batch normalization to prevent overfitting.
   • Monitor training with callbacks and visualize using TensorBoard.

7. Model Evaluation and Tuning:

   • Evaluate the model on the validation set and fine-tune hyperparameters for optimal performance.
   • Use metrics like accuracy, precision, recall, and F1-score to assess results.

8. Test Set Evaluation:

   • Evaluate the final model on a separate test set to simulate real-world performance.

9. Deployment:

   • Save the model architecture and weights.
   • Integrate the model with OpenCV to process images in real time.

10. Performance Enhancement:

    • Experiment with advanced architectures (e.g., transfer learning with pre-trained models) to boost accuracy.
    • Implement data augmentation techniques to further enhance model robustness.

11. Documentation and Reporting:

    • Document the entire process, including preprocessing steps, model architecture, and training parameters.
    • Provide an overview of achieved results and potential areas for improvement.

​

Technologies Used

Frameworks and Technologies:

• OpenCV: Utilized for image reading, preprocessing, and real-time image processing.
• TensorFlow and Keras: Employed for model creation, training, and evaluation.
• CNN: Leveraged for its effectiveness in image feature extraction and classification.

Repository

https://github.com/nb0309/trafficsignrecognition

Collaborators