This project involves developing a Traffic Sign Detection and Recognition system using a deep learning model built with Keras. Its goal is interpreting traffic signs in real-time.
Traffic Sign Recognizer is a project aimed at detecting and recognizing traffic signs using deep learning techniques. This system interprets traffic signs in real-time, enhancing the safety and efficiency of autonomous vehicles and driver assistance systems.
At this time detection for signs like unlimited is not as precise, but all other signs could be detected.
- Real-time traffic sign detection
- Traffic sign recognition using a deep learning model
- Built with Keras and Python
To install and run this project, follow these steps:
- Clone the repository:
git clone https://github.com/comhendrik/trafficsign_recognizer.git
- Navigate to the project directory:
cd trafficsign_recognizer - Create and activate a virtual environment (optional but recommended):
python3 -m venv env source env/bin/activate - Install the required dependencies:
pip install -r requirements.txt
This dataset is needed to train the model, feel free to modify the model to train it on a different dataset
To download the GTSRB (German Traffic Sign Recognition Benchmark) dataset, follow these steps:
Download the dataset from the official website: GTSRB Dataset Download Extract the downloaded zip file to a directory of your choice. Place the extracted dataset in the appropriate directory within the project.
To train the traffic sign recognizer, run the following command:
python3 load.pyTo use the traffic sign recognizer, run the following command:
python3 predict.pyThis will start the system and begin processing video input for traffic sign detection and recognition.
Contributions are welcome! Please fork this repository and submit pull requests.