Optimized Detection and Classification on GTRSB: Advancing Traffic Sign
  Recognition with Convolutional Neural Networks
Dhruv Toshniwal Saurabh Loya Anuj Khot Yash Marda
In the rapidly evolving landscape of transportation, the proliferation of
automobiles has made road traffic more complex, necessitating advanced
vision-assisted technologies for enhanced safety and navigation. These
technologies are imperative for providing critical traffic sign information,
influencing driver behavior, and supporting vehicle control, especially for
drivers with disabilities and in the burgeoning field of autonomous vehicles.
Traffic sign detection and recognition have emerged as key areas of research
due to their essential roles in ensuring road safety and compliance with
traffic regulations. Traditional computer vision methods have faced challenges
in achieving optimal accuracy and speed due to real-world variabilities.
However, the advent of deep learning and Convolutional Neural Networks (CNNs)
has revolutionized this domain, offering solutions that significantly surpass
previous capabilities in terms of speed and reliability. This paper presents an
innovative approach leveraging CNNs that achieves an accuracy of nearly 96\%,
highlighting the potential for even greater precision through advanced
localization techniques. Our findings not only contribute to the ongoing
advancement of traffic sign recognition technology but also underscore the
critical impact of these developments on road safety and the future of
autonomous driving.