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.

该研究项目旨在开发一个实时交通标志检测系统，使用YOLOv5架构并在郊区社区中进行高效的交通标志识别。项目的主要目标是在多样化的交通标志图像数据集上训练YOLOv5模型，并在适用于实时推断的硬件平台上部署该模型。该系统利用训练有素的YOLOv5模型，能够从车辆内部仪表盘上的实时摄像头中检测和分类交通标志。部署系统的性能将基于其交通标志检测的准确性、实时处理速度和整体可靠性进行评估。在郊区社区的案例研究中，该系统在检测交通标志方面显示出显著的96%准确率。该研究的发现有潜力通过提供及时准确的实时交通标志信息来改善道路安全和交通管理，并为自动驾驶的进一步研究铺平道路。

实时交通标志检测：圣克拉拉郊区案例研究