A machine learning model for detection and recognition of road traffic signs.

Abstract

ovice drivers often face challenges when it comes to understanding and interpreting traffic signs on the road. This lack of knowledge can lead to accidents and other road mishaps. To address this problem, we propose the development of a traffic sign detection and recognition system using Convolutional Neural Networks (CNN) in machine learning. This system will use computer vision techniques to detect and recognize traffic signs on the road, and provide real-time feedback to the driver. The proposed system has the potential to improve road safety and reduce the number of accidents caused by novice drivers. In this report, we will provide an overview of the problem, present the proposed solution, and discuss the implementation details and evaluation of the system. We believe that this system can make a significant contribution to road safety, particularly for novice drivers, and we are excited to present our findings and results. In addition to improving road safety for novice drivers, the proposed traffic sign detection and recognition system has future prospects in terms of replacing human labor in traffic sign inventory and maintenance. The system can be trained to identify traffic signs that require maintenance or replacement, thus reducing the need for manual inspection by human workers. Furthermore, the system can be used as a data collection tool, providing valuable information about traffic sign usage and trends. These future prospects not only improve the efficiency of traffic sign management but also reduce costs and improve overall road safety.