ReinforceNet: A reinforcement learning embedded object detection framework with region selection network
文献类型：期刊论文
| 作者 | Zhou, Man1,2; Wang, Rujing1 ; Xie, Chengjun1; Liu, Liu1,2; Li, Rui1,2; Wang, Fangyuan1,2; Li, Dengshan1,2
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| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2021-07-05 |
| 卷号 | 443 |
| 关键词 | Reinforcement learning Convolutional neural network Region selection network Object detection |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2021.02.073 |
| 通讯作者 | Xie, Chengjun(cjxie@iim.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | NEURAL-NETWORKS |
| 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] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000647206400011 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/122182]  |
| 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). |
入库方式： OAI收割
来源：合肥物质科学研究院
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