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Active object detection with multistep action prediction using deep q-network
Han XN(韩小宁)1; Liu HP(刘华平)1; Sun FC(孙富春)2; Zhang, Xinyu3
Department空间自动化技术研究室
Source PublicationIEEE Transactions on Industrial Informatics
ISSN1551-3203
2019
Volume15Issue:6Pages:3723-3731
Indexed BySCI ; EI
EI Accession number20192507070460
WOS IDWOS:000471725400057
Contribution Rank1
KeywordActive object detection active vision deep Q-learning network (DQN) dueling architecture reinforcement learning
AbstractIn recent years, great success has been achieved in visual object detection, which is one of the fundamental tasks in the field of industrial intelligence. Most of existing methods have been proposed to deal with single well-captured still images, while in practical robotic applications, due to nuisances, such as tiny scale, partial view, or occlusion, one still image may not contain enough information for object detection. However, an intelligent robot has the capability to adjust its viewpoint to get better images for detection. Therefore, active object detection becomes a very important perception strategy for intelligent robots. In this paper, by formulating active object detection as a sequential action decision process, a deep reinforcement learning framework is established to resolve it. Furthermore, a novel deep Q-learning network (DQN) with a dueling architecture is proposed, the network has two separate output channels, one predicts action type and the other predicts action range. By combining the two output channels, the action space is explored more efficiently. Several methods are extensively validated and the results show that the proposed one obtains the best results and predicts action in real time.
Language英语
WOS SubjectAutomation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Industrial
WOS KeywordRECONGITION
WOS Research AreaAutomation & Control Systems ; Computer Science ; Engineering
Funding ProjectNational Key R&D Program of China[2018YFB1004703] ; National Key R&D Program of China[2018YFB1004703] ; China NSF[61672349] ; China NSF[61672349] ; China NSF[61672353] ; China NSF[61672353] ; China NSF[61373155] ; China NSF[61373155] ; 2017 CCF-IFAA research fund[Z50201800178] ; 2017 CCF-IFAA research fund[Z50201800178] ; National Science Foundation of China ; German Research Foundation[NSFC 61621136008/DFG TRR-169] ; German Research Foundation[91848206] ; German Research Foundation[U1613212] ; National Key R&D Program of China[2018YFB1004703] ; National Key R&D Program of China[2018YFB1004703] ; China NSF[61672349] ; China NSF[61672349] ; China NSF[61672353] ; China NSF[61672353] ; China NSF[61373155] ; China NSF[61373155] ; 2017 CCF-IFAA research fund[Z50201800178] ; 2017 CCF-IFAA research fund[Z50201800178] ; National Science Foundation of China ; German Research Foundation[NSFC 61621136008/DFG TRR-169] ; German Research Foundation[91848206] ; German Research Foundation[U1613212]
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Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/24943
Collection空间自动化技术研究室
Corresponding AuthorLiu HP(刘华平)
Affiliation1.State Key Laboratory of Robotics Shenyang Institute of Automation, Chinese Academy of Sciences University of Chinese Academy of Sciences, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
2.Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
3.State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, China
Recommended Citation
GB/T 7714
Han XN,Liu HP,Sun FC,et al. Active object detection with multistep action prediction using deep q-network[J]. IEEE Transactions on Industrial Informatics,2019,15(6):3723-3731.
APA Han XN,Liu HP,Sun FC,&Zhang, Xinyu.(2019).Active object detection with multistep action prediction using deep q-network.IEEE Transactions on Industrial Informatics,15(6),3723-3731.
MLA Han XN,et al."Active object detection with multistep action prediction using deep q-network".IEEE Transactions on Industrial Informatics 15.6(2019):3723-3731.
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