Real-Time Traffic Sign Detection: A Case Study in a Santa Clara Suburban Neighborhood

• URL: http://arxiv.org/abs/2310.09630v1
• Date: Sat, 14 Oct 2023 17:52:28 GMT
• Title: Real-Time Traffic Sign Detection: A Case Study in a Santa Clara Suburban Neighborhood
• Authors: Harish Loghashankar, Hieu Nguyen
• Abstract summary: 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. The performance of the deployed system will be evaluated based on its accuracy in detecting traffic signs, real-time processing speed, and overall reliability.
• Score: 2.4087090457198435
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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