Introduction

This project aims to develop a deep learning model to accurately recognize German traffic signs using the German Traffic Sign Recognition Benchmark (GTSRB) dataset. The model is built using various preprocessing techniques, including grayscaling, histogram equalization, and normalization, to improve the performance of the classifier.

Dataset

The GTSRB dataset contains over 50,000 images of traffic signs belonging to 43 different classes. The dataset is split into three parts: training, validation, and testing sets.

Preprocessing

The images in the dataset undergo the following preprocessing steps:

1. Grayscaling: Convert the images to grayscale to reduce the complexity and computational cost.
2. Histogram equalization: Enhance the contrast of the images, making them more distinguishable.
3. Normalization (optional): Scale the pixel values to a specific range (e.g., [0, 1]) to improve the convergence speed of the model.

Model

The deep learning model can be built using popular frameworks such as TensorFlow or PyTorch. The choice of architecture depends on the specific requirements of the project, such as the desired level of accuracy and the computational resources available.

Usage

1. Clone the repository.
2. Install the required dependencies (e.g., TensorFlow, NumPy, OpenCV).
3. Run the preprocessing script to preprocess the dataset.
4. Train the model using the preprocessed dataset.
5. Evaluate the performance of the model on the testing set.

Results

For a comprehensive understanding of the results and explanations, refer to the paper "Traffic sign classification comparison between various convolution neural network models" available at https://pureportal.coventry.ac.uk/en/publications/traffic-sign-classification-comparison-between-various-convolutio. If you find this resource helpful, please cite it as "Traffic sign classification comparison between various convolution neural network models."