CNN-Based Model for Pose Detection of Industrial PCB

doi:10.1109/ICICTA.2017.93

CORC > 重庆绿色智能技术研究院 > 中国科学院重庆绿色智能技术研究院 > 机器人与3D打印技术创新中心

	CNN-Based Model for Pose Detection of Industrial PCB
	Li Haochen; Zheng Bin; Sun Xiaoyong; Zhao Yongting
	2017
会议日期	OCT 09-10, 2017
会议地点	Changsha, PEOPLES R CHINA
DOI	10.1109/ICICTA.2017.93
页码	390-393
通讯作者	Zheng, B ; Sun, XY (reprint author), Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China. ; Sun, XY (reprint author), Chongqing De Ling Technol Co Ltd, Chongqing 400713, Peoples R China.
英文摘要	For applications in robot manipulate with object, get the pose of objects is very important for controller's subsequent operations, especially in PCB feeding and blanking field, the grasp success rate will be enhanced if robot can get a exact pose of objects that relative to end manipulator. So in this paper we utilize the CNN model to build on a neural network for 3 tasks: object recognition, location and pose detection. This model treat pose detection as a classification problem and try to combine recognition, location at the same level. To validate the performance of the multi-task detection model, experiments and analysis of the model performance was carried out by the real-time PCB detection test. In the experiment, we use the PCB dataset comprised of 3 types which contains different poses made by ourselves as train/test samples. The number of object pose categories was divided into 8bins, 12bins and 36bins according to pose detection precision. We analysis the effect of the non-uniform datasets on training process and the final detect results shows that this CNN-based detection model can achieve high accuracy on PCB pose detection.
会议录	2017 10TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION (ICICTA 2017)
语种	英语
ISSN号	1949-1263
WOS记录号	WOS:000417429000086
内容类型	会议论文
源URL	[http://119.78.100.138/handle/2HOD01W0/370]
专题	机器人与3D打印技术创新中心
作者单位	(1) Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China; (2) Univ Chinese Acad Sci, Beijing 100049, Peoples R China; (3) Chongqing De Ling Technol Co Ltd, Chongqing 400713, Peoples R China
推荐引用方式 GB/T 7714	Li Haochen,Zheng Bin,Sun Xiaoyong,et al. CNN-Based Model for Pose Detection of Industrial PCB[C]. 见:. Changsha, PEOPLES R CHINA. OCT 09-10, 2017.