Towards Real-Time Traffic Sign Recognition via YOLO on a Mobile GPU

Abstract

Classification of objects in the video stream with the help of deep learning has gained immense popularity nowadays. Considering many systems solving the classification problem, the mobility is often required. This paper proposes an implementation of the YOLO (You Only Look Once) convolutional neural network to solve the problem of classification of traffic signs on the mobile platform NVIDIA Jetson. The main feature of this platform is the availability of mobile graphics processor NVIDIA Tegra, which allows high-performance computing with low power consumption. The implemented algorithm of the YOLO CNN neural network allows solving the problem of the classification of traffic signs in a continuous video stream with decent accuracy and speed, and the NVIDIA Jetson platform provides mobility of the system.