In the future, autonomous vehicles need to detect and identify traffic signs, and figure out their locations. Traffic sign recognition systems may play a crucial role in self-driving cars, artificial driver assistance, traffic surveillance as well as traffic safety. Accordingly, traffic sign recognition is an important area of research that triggered many research studies. Nowadays, more and more object recognition tasks are solved using Convolutional Neural Networks (CNN). Due to their high recognition rate and fast execution, CNNs have enhanced most computer vision tasks, both those that are already in place and those that are new. Many researchers exploited CNNs (among other machine learning and deep learning models) for traffic sign recognition and experimented with numerous datasets. Nevertheless, this is still an open research area in which many other models and datasets can be explored. In this paper, we compare VGG-16 and InceptionV3 CNN models for this purpose and experiment using two different datasets. Experimental results showed that VGG-16 outperforms InceptionV3 with 97% accuracy on one dataset, but suffer from overfitting on the other. This will be handled in future work.

Fatemah AbuAleenain, Hams Rashedi, Hanan Elazhary,"Comparison of VGG-16 and InceptionV3 for Traffic Sign Detection and Classification " International Journal of Advanced Multidisciplinary Research and Educational Development, V2(4): Page(355-361) July - August 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.