Intelligent Traffic Sign Detection and Recognition Using Computer Vision

The rapid development of cities and the increase in traffic on the roads require the development of traffic management systems that will improve traffic flow. This article aims to introduce the concept of intelligent driving and computer vision technology in this context. The system uses deep learning technology to identify and recognize traffic signs quickly, efficiently and accurately. Initially, the system captures the image from the camera input and uses a pre-processing to improve the quality of the image. Optical character recognition (OCR) and character recognition are used to recognize letters and symbols on signs, providing drivers and road users with valuable information. Real-time processing is achieved through optimization involving Region Proposal Network (RPN) and sliding window techniques. Additionally, the system provides constant awareness of the situation by using tracking algorithms to monitor the movement of traffic signs in the camera view. Distribution of traffic awareness signs for physical purposes such as correcting traffic signals, warning drivers of hazards, or collecting information for vehicle identification. The system integrates with existing traffic management systems to improve overall road safety and efficiency. Test results, in addition to real-life results, show that the body is strong in a variety of light and weather conditions. Intelligent railway vision and recognition systems represent a significant advance in the use of computer vision in transportation and helps achieve safer and more efficient routes.