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Multi-task learning for dangerous object detection in autonomous driving
Chen, Yaran1,2; Zhao, Dongbin1,2; Lv, Le1,2; Zhang, Qichao1,2
AbstractRecently, 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.
KeywordDangerous Object Detection Autonomous Driving Multi-task Learning Convolutional Neural Network
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationNational Natural Science Foundation of China (NSFC)(61573353 ; National Key Research and Development Plan(2016YFB0101000) ; 61533017)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000424188400035
Citation statistics
Cited Times:42[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.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
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
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