ADPNet: Attention based dual path network for lane detection

doi:10.1016/j.jvcir.2022.103574

CIOMP OpenIR

	ADPNet: Attention based dual path network for lane detection
	F. L. Ren; H. B. Zhou; L. Yang; F. L. Liu and X. He
	2022
发表期刊	Journal of Visual Communication and Image Representation
ISSN	1047-3203
卷号	87 页码:10
摘要	Recently, the task of lane detection has been greatly improved with the rapid development of deep learning and autonomous driving. However, there exist limitations like the challenging complex scenarios and real-time ef-ficiency. In this paper, we present a novel Attention Based Dual Path Network (ADPNet) to handle the task of lane detection. The ADPNet treat the process of lane detection as a task of binary semantic segmentation, where the Detail Path is designed to capture detailed low-level information and the Semantic Path with dual attention module is designed to capture contextual high-level information. We use the Feature Aggregation Module to fuse the information of the two paths, followed by the process of lane fitting to get a parametric description of lanes. The proposed ADPNet achieves good trade-off between the accuracy and real-time efficiency on TuSimple and CULane, which are two popular lane detection benchmark datasets. The results demonstrate that our architecture outperforms the current state-of-the-art methods.
DOI	10.1016/j.jvcir.2022.103574
URL	查看原文
收录类别	sci ; ei
语种	英语
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/66392
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	F. L. Ren,H. B. Zhou,L. Yang,et al. ADPNet: Attention based dual path network for lane detection[J]. Journal of Visual Communication and Image Representation,2022,87:10.
APA	F. L. Ren,H. B. Zhou,L. Yang,&F. L. Liu and X. He.(2022).ADPNet: Attention based dual path network for lane detection.Journal of Visual Communication and Image Representation,87,10.
MLA	F. L. Ren,et al."ADPNet: Attention based dual path network for lane detection".Journal of Visual Communication and Image Representation 87(2022):10.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
ADPNet Attention bas（7008KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览下载