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An improved algorithm based on deep learning network for road image redundancy removal

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成果类型:
期刊论文
作者:
Yang, Shengli*;Wang, Haoliang;Chen, Qiang
通讯作者:
Yang, Shengli
作者机构:
[Chen, Qiang; Yang, Shengli; Wang, Haoliang] Dongguan City Coll, Sch Comp & Informat, Dongguan 523419, Peoples R China.
通讯机构:
[Yang, Shengli] D
Dongguan City Coll, Sch Comp & Informat, Dongguan 523419, Peoples R China.
语种:
英文
关键词:
Contour image;Deep learning network;Image recognition;Redundancy removal;Vanishing point
期刊:
Journal of Supercomputing
ISSN:
0920-8542
年:
2022
卷:
78
期:
8
页码:
10385-10404
基金类别:
GDAS' Project of Building a World-class Research Institution in China [2020GDASYL-20200402007]
机构署名:
本校为第一且通讯机构
摘要:
Road detection is defined as one of the core technology of Advanced Driving Assistance System (ADAS), and this problem is important for improving the recognition accuracy and speed. Though much work has been done concerning road detection, the related questions about non-road areas are not thoroughly considered. Understanding the question is of primary importance in ADAS, we proposed an improved algorithm based on deep learning network for road image redundancy removal. Compared with the most typical road recognition methods, the experimental results show that the proposed method improves the ...

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