Computer Vision In Automated Road Safety Systems For Traffic Management

Abstract

The purpose of this project is to examine the feasibility of incorporating traffic sign identification into automated road safety systems for traffic control. This investigation is carried out using methods from computer vision. In order to preserve both driver safety and the smooth operation of traffic, traffic signs are necessary. In this research, traffic signals taken from live video streams are identified and categorized using convolutional neural networks, or CNNs. CNNs are employed to do this. Through the use of deep learning models trained on extensive annotated datasets, the system is able to reliably identify a broad variety of traffic signs, even in a variety of environmental conditions. For example, whether there is poor lighting or weather-related disruptions, the system can still identify traffic signs. The presented system separates signs from their surroundings using picture segmentation techniques. This ensures that the detecting procedure is carried out more accurately. The use of transfer learning, a technique that leverages previously learned models, greatly enhances the performance of this approach. The outcome of this work is an effective and scalable system. This technology contributes to the safety of drivers and passengers on the road by providing real-time notifications on speed limits, stop signs, and other regulatory indicators. It also offers applications for intelligent traffic management and autonomous driving, both of which are advantageous.