Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics
Lamane, Mohamed
Unknown Affiliation
Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Embedded deployment of traffic sign detection and recognition systems Taouqi, Imane; Lamane, Mohamed; Klilou, Abdessamad; Arsalane, Assia; Chaji, Kebir
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11215

Abstract

Traffic sign (TS) detection and recognition are essential components of advanced driver assistance systems (ADAS), contributing to safer and more reliable driving. However, deploying deep learning–based vision models on embedded platforms is challenging due to constraints in computational power and energy consumption. In this work, a comparative deployment of you only look once version 7 (YOLOv7) and YOLOv7-tiny deep learning algorithms is conducted on embedded NVIDIA platforms, namely Jetson Nano and Jetson Xavier NX, to evaluate their suitability for real-time TS detection. Following the detection stage, a convolutional neural network (CNN) is integrated to perform TS recognition, enabling a complete detection–recognition pipeline. Experimental results show that YOLOv7-tiny achieves higher detection precision of 97%, while providing better speed and computational cost on resource-constrained devices, with Jetson Nano reaching 18.8 frames per second (FPS) and, on Jetson Xavier NX reaching 43 FPS. The integrated CNN model ensures reliable classification of detected TS with an accuracy of 99.54%. This work highlights the trade-offs between precision, speed, and power consumption and provides practical guidance for selecting detection and recognition architectures for embedded ADAS applications.
Co-Authors Arsalane, Assia Chaji, Kebir Klilou, Abdessamad Taouqi, Imane