Traffic Sign Recognition Based On Scaled Convolutional Neural Network For Advanced Driver Assistance System
Riadh Ayachi, Mouna Afif, Yahia Said, Abdessalem Ben Abdelali · 2020
Advanced driver assistance system (ADAS) is one of the most important systems for human assistance. It assists the drivers to control the vehicle by providing essential information about the environment objects. In this paper, we propose a traffic signs recognition application for ADAS. The proposed application is based on the deep learning technique. In particular, we used the convolutional neural networks (CNN) to process the data provided by the system cameras. The proposed CNN was scaled in a way to get a light model size without decreasing the accuracy. The proposed CNN is suitable for embedded implementation while keeping high performance and real-time processing. The evaluation of the proposed CNN on the European dataset results in 99.32% accuracy and 250 FPS of inference speed when implemented on an Nvidia GTX960 GPU. The achieved results proved the efficiency of the scaling technique. It is a very good technique to get a small model size and high performance.