Authors :
Vedant Mahangade; Atharva Kulkarni; Siddhant Lodha; Atharva Awale
Volume/Issue :
Volume 7 - 2022, Issue 9 - September
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3T07mm7
DOI :
https://doi.org/10.5281/zenodo.7155146
Abstract :
Traffic signs are a crucial part of our road
environment. They provide crucial information,
sometimes compelling recommendations, to ensure that
driving behaviors are adjusted and that any currently
enforced traffic regulations are observed. With majority
of modern automobiles equipped with an automated
driving assistance systems a robust and efficient traffic
sign classifier would be considered a must. We propose a
Traffic Sign Recognition system which follows a neural
network-based approach that uses YOLOv3 (You Only
Look Once Version 3) as object detector rather than a
classifier followed by a CNN (Convolutional Neural
Network) to classify traffic signs. This approach of
dividing the modules to compute single task turns out to
improve the system’s performance even with limited
training thus providing a better platform for
development of models to solve similar tasks.
Keywords :
YOLOv3, CNN, Traffic Sign, Image, Classification, Detection, Recognition.
Traffic signs are a crucial part of our road
environment. They provide crucial information,
sometimes compelling recommendations, to ensure that
driving behaviors are adjusted and that any currently
enforced traffic regulations are observed. With majority
of modern automobiles equipped with an automated
driving assistance systems a robust and efficient traffic
sign classifier would be considered a must. We propose a
Traffic Sign Recognition system which follows a neural
network-based approach that uses YOLOv3 (You Only
Look Once Version 3) as object detector rather than a
classifier followed by a CNN (Convolutional Neural
Network) to classify traffic signs. This approach of
dividing the modules to compute single task turns out to
improve the system’s performance even with limited
training thus providing a better platform for
development of models to solve similar tasks.
Keywords :
YOLOv3, CNN, Traffic Sign, Image, Classification, Detection, Recognition.
