卷积神经网络
计算机科学
自动驾驶
人工智能
深度学习
面部识别系统
特征(语言学)
面子(社会学概念)
人工神经网络
计算机视觉
机器学习
特征提取
模式识别(心理学)
工程类
汽车工程
社会科学
语言学
哲学
社会学
作者
Xingzhen Tao,Haiping Li,Lei Deng
出处
期刊:Communications in computer and information science
日期:2022-01-01
卷期号:: 112-123
标识
DOI:10.1007/978-981-19-4109-2_12
摘要
With the continuous improvement of deep learning algorithms, it has achieved considerable results in the fields of machine vision and natural language processing. In the face of problems that cannot be solved by traditional methods or are not effective, deep learning can achieve more desirable results through its powerful feature learning and mastering the laws of the problem. The lane recognition based on a convolutional neural network is proposed to address the problems of lane crushing of self-driving intelligent vehicles and the accuracy of traffic marker recognition, respectively. The traffic marker recognition model based on YOLO5, by collecting data and completing model training, finally achieves the average lane crush of the self-driving car less than 1 time, and the traffic marker recognition rate reaches 98.5%.
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