CNN-Based Model for Pose Detection of Industrial PCB
文献类型：会议论文
| 作者 | Li Haochen; Zheng Bin ; Sun Xiaoyong; Zhao Yongting
|
| 出版日期 | 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 |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/370]  |
| 专题 | 北斗导航工程中心 |
| 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. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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