计算机科学
聚类分析
人工智能
深度学习
集合(抽象数据类型)
计算机视觉
交通信号灯
目标检测
实时计算
数据集
机器人
模式识别(心理学)
程序设计语言
作者
Huy Khanh Hua,Khang Hoang Nguyen,Luyl-Da Quach,Hoang Ngoc Tran
标识
DOI:10.1145/3591569.3591589
摘要
One of the most significant uses of autonomous cars in recent years is the detection of traffic light signals. Deep learning technology, which has a number of benefits including high detection accuracy and quick response to changes, is supporting the development of traffic light recognition under various environmental situations. In this paper, we use two methods to improve the traffic light detection and recognition method. First, we speed up training time by using the K-means clustering algorithm to compress image data. Second, a real time traffic light signal (red, yellow, green) identity based on the You Only Look Once (Yolov5) model is introduced. We utilised a variety of datasets including a freely available Roboflow dataset, a set of data obtained from Gazebo simulator, and a traffic light of CanTho city dataset to train and evaluate the proposed system. Furthermore, our algorithm was validated on a vehicle model in a simulated environment Gazebo of Robot Operating System 2 (ROS2).
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