Multiview Machine Vision Research of Fruits Boxes Handling Robot Based on the Improved 2D Kernel Principal Component Analysis Network
In order to better realize the orchard intelligent mechanization and reduce the labour intensity of workers, the study of intelligent fruit boxes handling robot is necessary. The first condition to realize intelligence is the fruit boxes recognition, which is the research content of this paper. The...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2021/3584422 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832560709463965696 |
|---|---|
| author | Xinning Li Hu Wu Xianhai Yang Peng Xue Shuai Tan |
| author_facet | Xinning Li Hu Wu Xianhai Yang Peng Xue Shuai Tan |
| author_sort | Xinning Li |
| collection | DOAJ |
| description | In order to better realize the orchard intelligent mechanization and reduce the labour intensity of workers, the study of intelligent fruit boxes handling robot is necessary. The first condition to realize intelligence is the fruit boxes recognition, which is the research content of this paper. The method of multiview two-dimensional (2D) recognition was adopted. A multiview dataset for fruits boxes was built. For the sake of the structure of the original image, the model of binary multiview 2D kernel principal component analysis network (BM2DKPCANet) was established to reduce the data redundancy and increase the correlation between the views. The method of multiview recognition for the fruits boxes was proposed combining BM2DKPCANet with the support vector machine (SVM) classifier. The performance was verified by comparing with principal component analysis network (PCANet), 2D principal component analysis network (2DPCANet), kernel principal component analysis network (KPCANet), and binary multiview kernel principal component analysis network (BMKPCANet) in terms of recognition rate and time consumption. The experimental results show that the recognition rate of the method is 11.84% higher than the mean value of PCANet though it needs more time. Compared with the mean value of KPCANet, the recognition rate exceeded 2.485%, and the time saved was 24.5%. The model can meet the requirements of fruits boxes handling robot. |
| format | Article |
| id | doaj-art-f913762d26264b18a4ca7ef0adccfe2a |
| institution | Kabale University |
| issn | 1687-9600 1687-9619 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-f913762d26264b18a4ca7ef0adccfe2a2025-02-03T01:26:59ZengWileyJournal of Robotics1687-96001687-96192021-01-01202110.1155/2021/35844223584422Multiview Machine Vision Research of Fruits Boxes Handling Robot Based on the Improved 2D Kernel Principal Component Analysis NetworkXinning Li0Hu Wu1Xianhai Yang2Peng Xue3Shuai Tan4School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, ChinaSchool of Mechanical Engineering, Shandong University of Technology, Zibo 255000, ChinaSchool of Mechanical Engineering, Shandong University of Technology, Zibo 255000, ChinaNational Engineering Research Center for Production Equipment, Dongying 257091, ChinaNational Engineering Research Center for Production Equipment, Dongying 257091, ChinaIn order to better realize the orchard intelligent mechanization and reduce the labour intensity of workers, the study of intelligent fruit boxes handling robot is necessary. The first condition to realize intelligence is the fruit boxes recognition, which is the research content of this paper. The method of multiview two-dimensional (2D) recognition was adopted. A multiview dataset for fruits boxes was built. For the sake of the structure of the original image, the model of binary multiview 2D kernel principal component analysis network (BM2DKPCANet) was established to reduce the data redundancy and increase the correlation between the views. The method of multiview recognition for the fruits boxes was proposed combining BM2DKPCANet with the support vector machine (SVM) classifier. The performance was verified by comparing with principal component analysis network (PCANet), 2D principal component analysis network (2DPCANet), kernel principal component analysis network (KPCANet), and binary multiview kernel principal component analysis network (BMKPCANet) in terms of recognition rate and time consumption. The experimental results show that the recognition rate of the method is 11.84% higher than the mean value of PCANet though it needs more time. Compared with the mean value of KPCANet, the recognition rate exceeded 2.485%, and the time saved was 24.5%. The model can meet the requirements of fruits boxes handling robot.http://dx.doi.org/10.1155/2021/3584422 |
| spellingShingle | Xinning Li Hu Wu Xianhai Yang Peng Xue Shuai Tan Multiview Machine Vision Research of Fruits Boxes Handling Robot Based on the Improved 2D Kernel Principal Component Analysis Network Journal of Robotics |
| title | Multiview Machine Vision Research of Fruits Boxes Handling Robot Based on the Improved 2D Kernel Principal Component Analysis Network |
| title_full | Multiview Machine Vision Research of Fruits Boxes Handling Robot Based on the Improved 2D Kernel Principal Component Analysis Network |
| title_fullStr | Multiview Machine Vision Research of Fruits Boxes Handling Robot Based on the Improved 2D Kernel Principal Component Analysis Network |
| title_full_unstemmed | Multiview Machine Vision Research of Fruits Boxes Handling Robot Based on the Improved 2D Kernel Principal Component Analysis Network |
| title_short | Multiview Machine Vision Research of Fruits Boxes Handling Robot Based on the Improved 2D Kernel Principal Component Analysis Network |
| title_sort | multiview machine vision research of fruits boxes handling robot based on the improved 2d kernel principal component analysis network |
| url | http://dx.doi.org/10.1155/2021/3584422 |
| work_keys_str_mv | AT xinningli multiviewmachinevisionresearchoffruitsboxeshandlingrobotbasedontheimproved2dkernelprincipalcomponentanalysisnetwork AT huwu multiviewmachinevisionresearchoffruitsboxeshandlingrobotbasedontheimproved2dkernelprincipalcomponentanalysisnetwork AT xianhaiyang multiviewmachinevisionresearchoffruitsboxeshandlingrobotbasedontheimproved2dkernelprincipalcomponentanalysisnetwork AT pengxue multiviewmachinevisionresearchoffruitsboxeshandlingrobotbasedontheimproved2dkernelprincipalcomponentanalysisnetwork AT shuaitan multiviewmachinevisionresearchoffruitsboxeshandlingrobotbasedontheimproved2dkernelprincipalcomponentanalysisnetwork |