Multi-task learning for dangerous object detection in autonomous driving

doi:10.1016/j.ins.2017.08.035

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 深度强化学习

	Multi-task learning for dangerous object detection in autonomous driving
	Chen, Yaran1,2 ; Zhao, Dongbin1,2 ; Lv, Le 1,2; Zhang, Qichao1,2
刊名	INFORMATION SCIENCES
	2018-03-01
卷号	432 期号:*页码:559-571
关键词	Dangerous Object Detection Autonomous Driving Multi-task Learning Convolutional Neural Network
DOI	10.1016/j.ins.2017.08.035
文献子类	Article
英文摘要	Recently, autonomous driving has been extensively studied and has shown considerable promise. Vision-based dangerous object detection is a crucial technology of autonomous driving. In previous work, dangerous object detection is generally formulated as a typical object detection problem and a distance-based danger assessment problem, separately. These two problems are usually dealt with using two independent models. In fact, vision based object detection and distance prediction present prominent visual relationship. The objects with different distance to the camera have different attributes (pose, size and definition), which are very worthy to be exploited for dangerous object detection. However, these characteristics are usually ignored in previous work. In this paper, we propose a novel multi-task learning (MTL) method to jointly model object detection and distance prediction with a Cartesian product-based multi-task combination strategy. Furthermore, we mathematically prove that the proposed Cartesian product-based combination strategy is more optimal than the linear multi-task combination strategy that is usually used in MTL models, when the multi-task itself is not independent. Systematic experiments show that the proposed approach consistently achieves better object detection and distance prediction performances compared to both the single-task and multi-task dangerous object detection methods. (C) 2017 Elsevier Inc. All rights reserved.
WOS关键词	RECOGNITION ; VEHICLES ; SYSTEM ; FUSION
WOS研究方向	Computer Science
语种	英语
WOS记录号	WOS:000424188400035
资助机构	National Natural Science Foundation of China (NSFC)(61573353 ; National Key Research and Development Plan(2016YFB0101000) ; 61533017)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/15664]
专题	复杂系统管理与控制国家重点实验室_深度强化学习
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, 19A Yuquan Rd, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Chen, Yaran,Zhao, Dongbin,Lv, Le,et al. Multi-task learning for dangerous object detection in autonomous driving[J]. INFORMATION SCIENCES,2018,432(*):559-571.
APA	Chen, Yaran,Zhao, Dongbin,Lv, Le,&Zhang, Qichao.(2018).Multi-task learning for dangerous object detection in autonomous driving.INFORMATION SCIENCES,432(*),559-571.
MLA	Chen, Yaran,et al."Multi-task learning for dangerous object detection in autonomous driving".INFORMATION SCIENCES 432.*(2018):559-571.