This project focuses on Traffic Sign Detection using Convolutional Neural Networks with an emphasis on data augmentation.

• Dataset: German Traffic Signs
• GitHub Repository: Traffic_Sign_Detection_CNN_Data_Augmentation

1. Clone the GitHub repository:

   git clone https://github.com/mrunmayee17/Traffic_Sign_Detection_CNN_Data_Augmentation.git
   

•   Install the required libraries, including TensorFlow and Keras.
  

•   Execute the provided Jupyter Notebook (main.ipynb) to run the project.
  

Key Steps

•   Loading the Dataset: The project uses the German Traffic Signs dataset, which contains various traffic sign images.
  

Reference file: utility/load_dataset.py

•   Data Preprocessing: Images are preprocessed by applying gray scaling and histogram equalization.
  

Reference file: utility/preprocess_image.py

•   Data Augmentation: Data augmentation techniques are used to increase the size of the training dataset.
  

  

•   Model Building: A Convolutional Neural Network (CNN) model is defined for traffic sign detection.
  

Reference file: utility/load_model.py

•   Training: The CNN model is trained using the training dataset, with validation on a separate validation dataset.
  

•   Evaluation: The model's performance is evaluated using test data, and metrics like accuracy and loss are plotted.
  

•   Prediction: The trained model is used to predict traffic signs on sample images.
  

Results

• The project aims to improve road safety and traffic management by enhancing real-time traffic sign recognition.
• Various plots and visualizations are provided to understand the dataset and the model's performance.
• The accuracy of the model is evaluated on both the test dataset and custom traffic sign images. References
• The project utilizes the German Traffic Signs dataset and various Python libraries, including TensorFlow and Keras.
• The code includes data preprocessing, data augmentation, model building, training, and evaluation steps for traffic sign detection.