Published Paper Details:

The rapid advancement of autonomous vehicle technology necessitates robust and efficient systems for traffic sign recognition. In this project, we introduce a novel approach that leverages advanced deep learning techniques to enhance the accuracy and real-time performance of traffic sign detection in autonomous vehicles.Our method combines state-of-the-art convolutional neural networks (CNNs) with innovative data augmentation and transfer learning strategies. We have meticulously curated and annotated a comprehensive dataset encompassing a wide range of real-world traffic sign scenarios. Through extensive experimentation and fine-tuning, our model exhibits remarkable generalization and adaptability, even in challenging lighting and weather conditions.The key contributions of this research lie in the development of a highly efficient deep learning model specifically tailored for traffic sign recognition, as well as the creation of a benchmark dataset for evaluation. Our approach not only surpasses existing benchmarks in terms of accuracy but also demonstrates impressive real-time performance, making it a promising solution for safe and reliable autonomous driving.Furthermore, we discuss the potential applications of our model beyond autonomous vehicles, including intelligent transportation systems and traffic management. Our work represents a significant step forward in the pursuit of safer and more dependable autonomous driving technology.