TY  - JOUR
AU  - S Ramana Reddy
AU  - Y Raj Kumar
AU  - T Sai Kiran
AU  - T Tarun
PY  - 2025
DA  - 2025/06/27
TI  - Deep Learning Approaches For Traffic Sign Detection
JO  - Global Journal of Engineering Innovations and Interdisciplinary Research
VL  - 5
IS  - 4
AB  - Detecting traffic signs is important for many purposes, like ensuring safe driving and identifying illegal
driving behavior. This study assesses specialized tools (e.g., Resnet, Inception, Mobile-net, and Dark net)
and sophisticated detection models. It improves the trained prior model using the Microsoft Coco dataset
for German car sign detection by using adaptive learning. evaluation of image size, computational
complexity, speed, memory utilization, and accuracy. The results show that R-Fcn Resnet 101 balances
precision and quick performance While Ssd Mobile-net is the fastest and best memory, Yolo Version 2
excels in performance & precision making it perfect for embedded devices, mobile devices, and gadgets.
SN  - 3066-1226
UR  - https://dx.doi.org/10.33425/3066-1226.1134
DO  - 10.33425/3066-1226.1134