Traffic Sign Detection and Classification

Traffic signs play a crucial role in conveying important information to drivers on the road, including speed limits, turn regulations, and other rules. Traffic Sign Detection technology has been developed to assist drivers in complying with traffic rules, enabling early warning to avoid rule violations. However, existing traffic sign detection systems have limitations such as incorrect predictions and high hardware and maintenance costs. The proposed system uses a deep learning algorithm to identify and classify traffic signs, with a web camera feature that detects and displays screens for closer viewing. This helps drivers save valuable moments by reducing the probability of rule-breaking-related accidents. Advanced technologies like convolutional neural networks (CNN) can enhance the performance of traffic sign detection systems, providing accurate and reliable information in real time. This paper presents new innovations in PC vision and transportation engineering by using the German Traffic Sign Recognition (GTSRB) dataset to represent different types of traffic signals in the world's space. The paper presents a unique CNN architecture designed specifically for traffic signal models, showing a deep understanding of the specific capabilities and problems associated with traffic signals, potentially leading to the accuracy of the first class. The findings may also reveal a noticeable improvement in overall performance metrics, highlighting the effectiveness of the proposed method