Recognition of traffic signs by convolutional neural nets for self-driving vehicles

Article type: Research Article

Authors: Farag, Wael^{a; b}

Affiliations: [a] College of Engineering and Technology, American University of the Middle East, Eqaila, Kuwait | [b] Electrical Power Engineering Department, Cairo University, Cairo, Egypt | E-mail: wael.farag@aum.edu.kw, wael.farag@cu.edu.eg

Correspondence: [*] Corresponding author: Electrical Power Engineering Department, Cairo University, Cairo, Egypt. E-mail: wael.farag@aum.edu.kw,wael.farag@cu.edu.eg.

Abstract: In this paper, a comprehensive Convolutional Neural Network (CNN) based classifier “WAF-LeNet” is proposed and developed to be used in traffic signs recognition and identification as an empowerment of autonomous driving technologies. The implemented architecture is a deep fifteen-layer network that has been selected after extensive trials to be fast enough to suit the designated application. The CNN got trained using Adam’s optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. The learning process is carried out using the well-known “German Traffic Sign Dataset – GTSRB”. The data has been partitioned into training, validation and testing data sets. Additionally, more random traffic signs images are collected from the web and further used to test the robustness of the proposed CNN classifier. The paper goes through the development process in details and shows the image processing pipeline harnessed in the development. The proposed approach proved successful in identifying correctly 96.5% of the testing data set and 100% of the robustness data set with much smaller and faster network than other counterparts.

Keywords: Deep learning, convolutional neural network, adam optimization, self-driving car, traffic sign recognition

DOI: 10.3233/KES-180385

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 22, no. 3, pp. 205-214, 2018