ParallelEye Pipeline: An Effective Method to Synthesize Images for Improving the Visual Intelligence of Intelligent Vehicles

doi:10.1109/TSMC.2023.3273896

	ParallelEye Pipeline: An Effective Method to Synthesize Images for Improving the Visual Intelligence of Intelligent Vehicles
	Li, Xuan 1; Wang, Kunfeng2 ; Gu, Xianfeng 3; Deng, Fang 4; Wang, Fei-Yue5
发表期刊	IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
ISSN	2168-2216
	2023-05-16
页码	12
通讯作者	Wang, Kunfeng(wangkf@mail.buct.edu.cn)
摘要	Virtual simulated scenes are becoming a critical part of autonomous driving. In the context of knowledge automation and machine learning, simulated images are widely used for visual environmental perception. However, even the most inspirational applications have not fully exploited the potential of simulated images in solving real-world problems. In this article, we propose a novel framework "ParallelEye Pipeline", which uses image-to-image translation and simulated images to automatically generate realistic synthetic images with multiple ground-truth annotations. Specifically, this method has three steps: first, we use Unity3D software to simulate driving scenarios and generate simulated image pairs (including raw images and six ground-truth labels) from the simulated scenes; second, advanced image-to-image translation algorithms can generate realistic and high-resolution synthetic images from simulated image pairs; third, we exploit publicly datasets, simulated images, and synthetic images to conduct experiments for visual perception. The experimental results suggest: 1) synthetic images and simulated images can improve the performance of detectors in real autonomous driving scenarios and 2) image-to-image translation algorithms can be affected by occlusion condition.
关键词	Annotations Pipelines Autonomous vehicles Generative adversarial networks Task analysis Semantics Visualization Generative adversarial network (GAN) intelligent vehicles object detection simulated scene synthetic image
DOI	10.1109/TSMC.2023.3273896
关键词[WOS]	VISION
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2020YFC2003900] ; National Natural Science Foundation of China[62076020] ; National Natural Science Foundation of China[62203250] ; Young Elite Scientists Sponsorship Program of China Association of Science and Technology[YESS20210289] ; China Postdoctoral Science Foundation[2020TQ1057] ; China Postdoctoral Science Foundation[2020M682823] ; China Scholarship Council
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Young Elite Scientists Sponsorship Program of China Association of Science and Technology ; China Postdoctoral Science Foundation ; China Scholarship Council
WOS研究方向	Automation & Control Systems ; Computer Science
WOS类目	Automation & Control Systems ; Computer Science, Cybernetics
WOS记录号	WOS:001005366200001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：12[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/173211/53505
专题	多模态人工智能系统全国重点实验室
通讯作者	Wang, Kunfeng
作者单位	1.Peng Cheng Lab, Dept Math & Theories, Shenzhen 518000, Peoples R China 2.Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China 3.SUNY Stony Brook, Dept Comp Sci, Stony Brook, NY 11794 USA 4.Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China 5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Li, Xuan,Wang, Kunfeng,Gu, Xianfeng,et al. ParallelEye Pipeline: An Effective Method to Synthesize Images for Improving the Visual Intelligence of Intelligent Vehicles[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,2023:12.
APA	Li, Xuan,Wang, Kunfeng,Gu, Xianfeng,Deng, Fang,&Wang, Fei-Yue.(2023).ParallelEye Pipeline: An Effective Method to Synthesize Images for Improving the Visual Intelligence of Intelligent Vehicles.IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,12.
MLA	Li, Xuan,et al."ParallelEye Pipeline: An Effective Method to Synthesize Images for Improving the Visual Intelligence of Intelligent Vehicles".IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2023):12.