@inproceedings{e9001af847a34b5690dd13935421e20e,
title = "Real-Time Traffic Sign Detection and Recognition for Enhanced Road Safety in Autonomous Driving: A Deep CNN-Based Approach",
abstract = "Traffic sign detection and recognition are critical tasks in autonomous driving systems and video-based methods are becoming increasingly popular due to the availability of high-resolution cameras. In this work, we propose a deep CNN-based approach that can detect and recognize traffic signs from video feeds in real-time. Our proposed approach consists of a multi-stage process that includes video pre-processing, object detection, and sign recognition using a deep CNN architecture. The model is trained on a large dataset of annotated video frames and evaluated its performance on a test set. The results indicate that the proposed method achieves high accuracy in detecting and recognizing traffic signs. This method has potential applications in autonomous driving systems and can contribute to improving road safety.",
author = "\{Nataraj Urs\}, \{H. D.\} and Aravind, \{B. N.\} and N. Yashwanth and Moumita Ruj",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 4th IEEE International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023 ; Conference date: 08-12-2023 Through 09-12-2023",
year = "2023",
doi = "10.1109/ICSTCEE60504.2023.10584947",
language = "English",
series = "Proceedings of the 4th IEEE International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Hulipalled, \{Vishwanath R.\} and S. Senthil and Devanathan M and Hemanth K.S",
booktitle = "Proceedings of the 4th IEEE International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023",
address = "United States",
}