A Two-Stream CNN With Simultaneous Detection and Segmentation for Robotic Grasping
文献类型：期刊论文
| 作者 | Yu, Yingying3,4 ; Cao, Zhiqiang3,4; Liu, Zhicheng3,4; Geng, Wenjie3,4; Yu, Junzhi2,4; Zhang, Weimin1
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| 刊名 | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
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| 出版日期 | 2022-02-01 |
| 卷号 | 52期号:2页码:1167-1181 |
| 关键词 | Grasping Robot kinematics Manipulators Image segmentation Deconvolution Machine learning Global deconvolution network (GDN) robotic grasping simultaneous detection and segmentation two-stream grasping convolutional neural network (CNN) |
| ISSN号 | 2168-2216 |
| DOI | 10.1109/TSMC.2020.3018757 |
| 通讯作者 | Cao, Zhiqiang(zhiqiang.cao@ia.ac.cn) |
| 英文摘要 | The manipulating robots receive much attention by offering better services, where object grasping is still challenging especially under background interferences. In this article, a novel two-stream grasping convolutional neural network (CNN) with simultaneous detection and segmentation is proposed. The proposed method is cascaded by an improved simultaneous detection and segmentation network BlitzNet and a two-stream grasping CNN TsGNet. The improved BlitzNet introduces the channel-based attention mechanism, and achieves an improvement of detection accuracy and segmentation accuracy with the combination of the learning of multitask loss weightings and background suppression. Based on the obtained bounding box and the segmentation mask of the target object, the target object is separated from the background, and the corresponding depth map and grayscale map are sent to TsGNet. By adopting depthwise separable convolution and designed global deconvolution network, TsGNet achieves the best grasp detection with only a small amount of network parameters. This best grasp in the pixel coordinate system is converted to a desired 6-D pose for the robot, which drives the manipulator to execute grasping. The proposed method combines a grasping CNN with simultaneous detection and segmentation to achieve the best grasp with a good adaptability to background. With the Cornell grasping dataset, the image-wise accuracy and object-wise accuracy of the proposed TsGNet are 93.13% and 92.99%, respectively. The effectiveness of the proposed method is verified by the experiments. |
| 资助项目 | National Natural Science Foundation of China[61633017] ; National Natural Science Foundation of China[61633020] ; National Natural Science Foundation of China[61836015] ; Beijing Advanced Innovation Center for Intelligent Robots and Systems[2018IRS21] |
| WOS研究方向 | Automation & Control Systems ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000742732900053 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; Beijing Advanced Innovation Center for Intelligent Robots and Systems |
| 源URL | [http://ir.ia.ac.cn/handle/173211/47915]  |
| 专题 | 中国科学院自动化研究所 |
| 通讯作者 | Cao, Zhiqiang |
| 作者单位 | 1.Beijing Inst Technol, Beijing Adv Innovat Ctr Intelligent Robots & Syst, Beijing 100081, Peoples R China 2.Peking Univ, State Key Lab Turbulence & Complex Syst, Dept Mech & Engn Sci, BIC ESAT Coll Engn, Beijing 100871, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yu, Yingying,Cao, Zhiqiang,Liu, Zhicheng,et al. A Two-Stream CNN With Simultaneous Detection and Segmentation for Robotic Grasping[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,2022,52(2):1167-1181. |
| APA | Yu, Yingying,Cao, Zhiqiang,Liu, Zhicheng,Geng, Wenjie,Yu, Junzhi,&Zhang, Weimin.(2022).A Two-Stream CNN With Simultaneous Detection and Segmentation for Robotic Grasping.IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,52(2),1167-1181. |
| MLA | Yu, Yingying,et al."A Two-Stream CNN With Simultaneous Detection and Segmentation for Robotic Grasping".IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 52.2(2022):1167-1181. |
入库方式： OAI收割
来源：自动化研究所
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