BiFNet: Bidirectional Fusion Network for Road Segmentation

doi:10.1109/TCYB.2021.3105488

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 深度强化学习

	BiFNet: Bidirectional Fusion Network for Road Segmentation
	Li, Haoran1,2 ; Chen, Yaran1,2 ; Zhang, Qichao1,2 ; Zhao, Dongbin1,2
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
	2021-08-31
页码	12
通讯作者	Zhao, Dongbin(dongbin.zhao@ia.ac.cn)
摘要	Multisensor fusion-based road segmentation plays an important role in the intelligent driving system since it provides a drivable area. The existing mainstream fusion method is mainly to feature fusion in the image space domain which causes the perspective compression of the road and damages the performance of the distant road. Considering the bird's eye views (BEVs) of the LiDAR remains the space structure in the horizontal plane, this article proposes a bidirectional fusion network (BiFNet) to fuse the image and BEV of the point cloud. The network consists of two modules: 1) the dense space transformation (DST) module, which solves the mutual conversion between the camera image space and BEV space and 2) the context-based feature fusion module, which fuses the different sensors information based on the scenes from corresponding features. This method has achieved competitive results on the KITTI dataset.
关键词	Roads Image segmentation Three-dimensional displays Cameras Laser radar Fuses Feature extraction Adaptive learning autonomous vehicles multisensor fusion road segmentation
DOI	10.1109/TCYB.2021.3105488
收录类别	SCI
语种	英语
资助项目	Beijing Municipal Natural Science Foundation[L191002] ; Beijing Science and Technology Plan[Z191100007419002] ; National Natural Science Foundation of China (NSFC)[61803371]
项目资助者	Beijing Municipal Natural Science Foundation ; Beijing Science and Technology Plan ; National Natural Science Foundation of China (NSFC)
WOS研究方向	Automation & Control Systems ; Computer Science
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000732313800001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	强化与进化学习
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/46870
专题	多模态人工智能系统全国重点实验室_深度强化学习
通讯作者	Zhao, Dongbin
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Coll Artificial Intelligence, Beijing 100049, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Li, Haoran,Chen, Yaran,Zhang, Qichao,et al. BiFNet: Bidirectional Fusion Network for Road Segmentation[J]. IEEE TRANSACTIONS ON CYBERNETICS,2021:12.
APA	Li, Haoran,Chen, Yaran,Zhang, Qichao,&Zhao, Dongbin.(2021).BiFNet: Bidirectional Fusion Network for Road Segmentation.IEEE TRANSACTIONS ON CYBERNETICS,12.
MLA	Li, Haoran,et al."BiFNet: Bidirectional Fusion Network for Road Segmentation".IEEE TRANSACTIONS ON CYBERNETICS (2021):12.