A Multi-Object Grasping Detection Based on the Improvement of YOLOv3 Algorithm
文献类型：会议论文
| 作者 | Du, Kun1,2; Song JL(宋吉来)1,3; Wang, Xiaofeng1; Li, Xiang1,2; Lin, Jie2 |
| 出版日期 | 2020 |
| 会议日期 | August 22-24, 2020 |
| 会议地点 | Hefei, China |
| 关键词 | YOLOv3 Robotic grasping Deep learning Corner detection Grasping position and pose detection |
| 页码 | 1027-1033 |
| 英文摘要 | YOLOv3 has achieved good results in the field of object detection. In order to achieve multi-object grasping detection, the network structure has been improved. The improved YOLOv3 algorithm is applied to the object position and pose detection in robotic grasping, and a deep learning model is proposed to predict the robot's grasping position, which can detect the occurrence of multiple objects in real time and grasp them in order according to the semantic information. For the specific application scenario, the corresponding dataset is made, and a corner detection method based on YOLOv3 is proposed to grasping position and pose detection. Compared with the traditional corner detection method, this method has semantic information in its detected corner. In the scene, we first classify and locate the object, then detect the corner of the object, and filter the corner of the false detection through the positioning of the object, and design the corresponding algorithm to complete the corner of the missed detection, so that the accuracy of the corner detection is greatly improved, reaching 99% in the self-made dataset. Finally, the position information of the corner is used to calculate the centroid position of the object, that is, the grasping point of the object. The point cloud information is obtained by depth camera, and the grasping pose of the object is calculated. This method can greatly improve the accuracy of grasping detection in specific scenes. |
| 源文献作者 | IEEE Control Systems Society (CSS) ; Northeastern University ; State Key Laboratory of Synthetical Automation for Process Industries ; Technical Committee on Control Theory, Chinese Association of Automation |
| 产权排序 | 2 |
| 会议录 | Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-7281-5854-9 |
| WOS记录号 | WOS:000621616901022 |
| 源URL | [http://ir.sia.cn/handle/173321/27695]  |
| 专题 | 沈阳自动化研究所_其他 |
| 通讯作者 | Du, Kun |
| 作者单位 | 1.Shenyang SIASUN Robot & Automation Co., LTD., Shenyang 110000, China 2.Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110000, China 3.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110000, China |
| 推荐引用方式 GB/T 7714 | Du, Kun,Song JL,Wang, Xiaofeng,et al. A Multi-Object Grasping Detection Based on the Improvement of YOLOv3 Algorithm[C]. 见:. Hefei, China. August 22-24, 2020. |
入库方式： OAI收割
来源：沈阳自动化研究所
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