Authors:
Rafael Marques
1
;
Tiago Ribeiro
1
;
Gil Lopes
2
and
A. Fernando Ribeiro
1
Affiliations:
1
Department of Industrial Electronics, ALGORITMI CENTER, University of Minho, Guimarães, Portugal
;
2
Department of Communication Sciences and Information Technologies, University of Maia, Maia, Portugal
Keyword(s):
Supervised Learning, YOLOv3, Traffic Sign Detection, Autonomous Mobile Robot, Robotics, Simulated Robot, Robocup.
Abstract:
Advanced Driver Assistance Systems (ADAS) relates to various in-vehicle systems intended to improve road traffic safety by assisting drivers with improved road awareness, inherent dangers and other drivers nearby. Traffic sign detection and recognition is an integral part of ADAS since these provide information about traffic rules, road conditions, route directions and assistance for safe driving. In addition, traffic sign detection and recognition are essential research topics for safe and efficient driving when considering intelligent transportation systems. An approach to traffic sign/light detection and recognition using YOLOv3 and YOLOv3_tiny is presented in this paper in two different environments. The first is on a simulated and real autonomous driving robot for RoboCup Portuguese Open Autonomous Driving Competition. The robot must detect both traffic signs and lights in real-time and behave accordingly. The second environment is on public roads. A computer vision system insid
e the car points to the road, detecting and classifying traffic signs/lights (T S/L) in different weather and lighting conditions. YOLOv3 and YOLOv3_tiny were tested on both environments with an extensive hyperparameters search. The final result showcases videos of the two algorithms on the two environments.
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