Fast Lane Detection Flexibly Adapting to Road Structure Information

Jianxun Shi, Junzheng Wang, Jing Li

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

Modern commonly used lane detection algorithms usually transform the problem into an instance segmentation problem, and the speed and scalability of the algorithm need to be improved. At the same time, such a method ignores the structural information of the lane itself, which is precisely the important feature information in complex situations such as lane occlusion. In this regard, we propose a new method for the expansion of high -speed, challenging scenarios and unstructured roads. We adopt a row-based selection method, which can significantly reduce the computational cost. In addition, we propose a new structural loss function for the lane structure information to model the continuity and curvature of the lane. Extensive experiments on the CULane dataset show that our method has superior speed and accuracy. At the same time, we test it in the actual unstructured scenario, which proves that the method has certain scalability and flexibility.

源语言英语
主期刊名Proceedings of the 41st Chinese Control Conference, CCC 2022
编辑Zhijun Li, Jian Sun
出版商IEEE Computer Society
6605-6609
页数5
ISBN(电子版)9789887581536
DOI
出版状态已出版 - 2022
活动41st Chinese Control Conference, CCC 2022 - Hefei, 中国
期限: 25 7月 202227 7月 2022

出版系列

姓名Chinese Control Conference, CCC
2022-July
ISSN(印刷版)1934-1768
ISSN(电子版)2161-2927

会议

会议41st Chinese Control Conference, CCC 2022
国家/地区中国
Hefei
时期25/07/2227/07/22

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