摘要
本文以自动驾驶小车为例,将深度学习技术运用于自动驾驶小车,在模拟的道路上,实现对交通标志识别的自动驾驶。本文采用TensorFlow深度学习框架,编写CNN结构模型,训练卷积神经网络,运用OpenCV图像处理技术,使用摄像头采集模拟道路与交通信号标志数据,通过处理器计算和处理,面对相应的交通信号标志,自动驾驶小车自动采取应对措施。测试结果表明,小车具有一定程度的自动驾驶与交通标志识别能力。
In this article,taking the self-driving car as an example,the deep learning technology is combined with the self-driving car to realize the automatic driving of traffic sign recognition on the simulated road.This article uses the TensorFlow deep learning framework,writes CNN structural models,trains convolutional neural networks,uses OpenCV image processing technology,uses cameras to collect simulated road and traffic signal sign data,calculates and processes through processors,and faces corresponding traffic signal signs.Smart cars take countermeasures automatically.The test results show that the car has a certain degree of automatic driving and traffic sign recognition capabilities.
作者
曾啸川
邓红卫
莫岚淋
陈一楠
贺迪
ZENG Xiao-chuan;DENG Hong-wei;MO Lan-lin;CHEN Yi-nan;HE Di(College of Computer Science and Technology,Hengyang Normal University,Hengyang Hunan 421002)
出处
《数字技术与应用》
2020年第7期131-134,共4页
Digital Technology & Application
基金
国家级大学生创新创业训练计划项目(201910546003)
湖南省大学生创新业训练计划项目(S201910546004)。
