ReinforceNet: A reinforcement learning embedded object detection framework with region selection network

doi:10.1016/j.neucom.2021.02.073

HFCAS OpenIR

	ReinforceNet: A reinforcement learning embedded object detection framework with region selection network
	Zhou, Man 1,2; Wang, Rujing1 ; Xie, Chengjun1 ; Liu, Liu 1,2; Li, Rui 1,2; Wang, Fangyuan 1,2; Li, Dengshan 1,2
	2021-07-05
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
通讯作者	Xie, Chengjun(cjxie@iim.ac.cn)
摘要	In recent years, researchers have explored reinforcement learning based object detection methods. However, existing methods always suffer from barely satisfactory performance. The main reasons are that current reinforcement learning based methods generate a sequence of inaccurate regions without a reasonable reward function, and regard the non-optimal one at the final step as the detection result for lack of an effective region selection and refinement strategy. To tackle the above problems, we propose a novel reinforcement learning based object detection framework, namely ReinforceNet, possessing the capability of the region selection and refinement by integrating reinforcement learning agents & rsquo; action space with Convolutional Neural Network based feature space. In ReinforceNet, we redevelop a reward function that enables the agent to be trained effectively and provide more accurate region proposals. In order to further optimize them, we design Convolutional Neural Network based region selection network (RS-net) and bounding box refinement network (BBR-net). Particularly, the former consists of two sub-networks: Intersection-of-Union network (IoU-net) and Completeness network (CPL-net) which are employed jointly for selecting the optimal region proposal. The latter aims to refine the selected one as the final result. Extensive experimental results on two standard datasets PASCAL VOC 2007 and VOC 2012 demonstrate that ReinforceNet is capable of improving the region selection and learning better agent action representations for reinforcement learning, resulting in the state-of-the-art performance. (c) 2021 Elsevier B.V. All rights reserved.
关键词	Reinforcement learning Convolutional neural network Region selection network Object detection
DOI	10.1016/j.neucom.2021.02.073
关键词[WOS]	NEURAL-NETWORKS
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[31401293] ; National Natural Science Foundation of China[31671586] ; National Natural Science Foundation of China[61773360]
项目资助者	National Natural Science Foundation of China
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000647206400011
出版者	ELSEVIER
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.hfcas.ac.cn:8080/handle/334002/122183
专题	中国科学院合肥物质科学研究院
通讯作者	Xie, Chengjun
作者单位	1.Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China 2.Univ Sci & Technol China, Hefei 230026, Peoples R China
推荐引用方式 GB/T 7714	Zhou, Man,Wang, Rujing,Xie, Chengjun,et al. ReinforceNet: A reinforcement learning embedded object detection framework with region selection network[J]. NEUROCOMPUTING,2021,443.
APA	Zhou, Man.,Wang, Rujing.,Xie, Chengjun.,Liu, Liu.,Li, Rui.,...&Li, Dengshan.(2021).ReinforceNet: A reinforcement learning embedded object detection framework with region selection network.NEUROCOMPUTING,443.
MLA	Zhou, Man,et al."ReinforceNet: A reinforcement learning embedded object detection framework with region selection network".NEUROCOMPUTING 443(2021).