Accurate Robotic Grasp Detection with Angular Label Smoothing

doi:10.1007/s11390-022-1458-5

	Accurate Robotic Grasp Detection with Angular Label Smoothing
	Shi, Min 1; Lu, Hao 1; Li, Zhao-Xin 2; Zhu, Deng-Ming 2; Wang, Zhao-Qi 2
	2023-09-01
发表期刊	JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
ISSN	1000-9000
卷号	38 期号:5 页码:1149-1161
摘要	Grasp detection is a visual recognition task where the robot makes use of its sensors to detect graspable objects in its environment. Despite the steady progress in robotic grasping, it is still difficult to achieve both real-time and high accuracy grasping detection. In this paper, we propose a real-time robotic grasp detection method, which can accurately predict potential grasp for parallel-plate robotic grippers using RGB images. Our work employs an end-to-end convolutional neural network which consists of a feature descriptor and a grasp detector. And for the first time, we add an attention mechanism to the grasp detection task, which enables the network to focus on grasp regions rather than background. Specifically, we present an angular label smoothing strategy in our grasp detection method to enhance the fault tolerance of the network. We quantitatively and qualitatively evaluate our grasp detection method from different aspects on the public Cornell dataset and Jacquard dataset. Extensive experiments demonstrate that our grasp detection method achieves superior performance to the state-of-the-art methods. In particular, our grasp detection method ranked first on both the Cornell dataset and the Jacquard dataset, giving rise to the accuracy of 98.9% and 95.6%, respectively at real-time calculation speed.
关键词	robotic grasp detection attention mechanism angular label smoothing anchor box deep learning
DOI	10.1007/s11390-022-1458-5
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018AAA010-3002] ; National Natural Science Foundation of China[62172392] ; National Natural Science Foundation of China[61702482] ; National Natural Science Foundation of China[61972379]
WOS研究方向	Computer Science
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Software Engineering
WOS记录号	WOS:001114345700011
出版者	SPRINGER SINGAPORE PTE LTD
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38480
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li, Zhao-Xin
作者单位	1.North China Elect Power Univ, Sch Control & Comp Engn, Beijing 102206, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Shi, Min,Lu, Hao,Li, Zhao-Xin,et al. Accurate Robotic Grasp Detection with Angular Label Smoothing[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2023,38(5):1149-1161.
APA	Shi, Min,Lu, Hao,Li, Zhao-Xin,Zhu, Deng-Ming,&Wang, Zhao-Qi.(2023).Accurate Robotic Grasp Detection with Angular Label Smoothing.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,38(5),1149-1161.
MLA	Shi, Min,et al."Accurate Robotic Grasp Detection with Angular Label Smoothing".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 38.5(2023):1149-1161.