  This research project aims to develop a real-time traffic sign detection
system using the YOLOv5 architecture and deploy it for efficient traffic sign
recognition during a drive in a suburban neighborhood. The project's primary
objectives are to train the YOLOv5 model on a diverse dataset of traffic sign
images and deploy the model on a suitable hardware platform capable of
real-time inference. The project will involve collecting a comprehensive
dataset of traffic sign images. By leveraging the trained YOLOv5 model, the
system will detect and classify traffic signs from a real-time camera on a
dashboard inside a vehicle. The performance of the deployed system will be
evaluated based on its accuracy in detecting traffic signs, real-time
processing speed, and overall reliability. During a case study in a suburban
neighborhood, the system demonstrated a notable 96% accuracy in detecting
traffic signs. This research's findings have the potential to improve road
safety and traffic management by providing timely and accurate real-time
information about traffic signs and can pave the way for further research into
autonomous driving.
