Target Recognition of Industrial Robots Using Machine Vision in 5G Environment
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jin, Z. | - |
dc.contributor.author | Liu, L. | - |
dc.contributor.author | Gong, D. | - |
dc.contributor.author | Li, L. | - |
dc.date.available | 2021-04-12T00:40:52Z | - |
dc.date.created | 2021-03-22 | - |
dc.date.issued | 2021-02-25 | - |
dc.identifier.issn | 1662-5218 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/80699 | - |
dc.description.abstract | The purpose is to solve the problems of large positioning errors, low recognition speed, and low object recognition accuracy in industrial robot detection in a 5G environment. The convolutional neural network (CNN) model in the deep learning (DL) algorithm is adopted for image convolution, pooling, and target classification, optimizing the industrial robot visual recognition system in the improved method. With the bottled objects as the targets, the improved Fast-RCNN target detection model's algorithm is verified; with the small-size bottled objects in a complex environment as the targets, the improved VGG-16 classification network on the Hyper-Column scheme is verified. Finally, the algorithm constructed by the simulation analysis is compared with other advanced CNN algorithms. The results show that both the Fast RCN algorithm and the improved VGG-16 classification network based on the Hyper-Column scheme can position and recognize the targets with a recognition accuracy rate of 82.34%, significantly better than other advanced neural network algorithms. Therefore, the improved VGG-16 classification network based on the Hyper-Column scheme has good accuracy and effectiveness for target recognition and positioning, providing an experimental reference for industrial robots' application and development. © Copyright © 2021 Jin, Liu, Gong and Li. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Frontiers Media S.A. | - |
dc.relation.isPartOf | Frontiers in Neurorobotics | - |
dc.title | Target Recognition of Industrial Robots Using Machine Vision in 5G Environment | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000627414900001 | - |
dc.identifier.doi | 10.3389/fnbot.2021.624466 | - |
dc.identifier.bibliographicCitation | Frontiers in Neurorobotics, v.15 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85102459254 | - |
dc.citation.title | Frontiers in Neurorobotics | - |
dc.citation.volume | 15 | - |
dc.contributor.affiliatedAuthor | Liu, L. | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | 5G environment | - |
dc.subject.keywordAuthor | artificial intelligence | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | industrial robot | - |
dc.subject.keywordAuthor | machine vision | - |
dc.subject.keywordPlus | 5G mobile communication systems | - |
dc.subject.keywordPlus | Convolution | - |
dc.subject.keywordPlus | Convolutional neural networks | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Image enhancement | - |
dc.subject.keywordPlus | Industrial robots | - |
dc.subject.keywordPlus | Object recognition | - |
dc.subject.keywordPlus | Classification networks | - |
dc.subject.keywordPlus | Complex environments | - |
dc.subject.keywordPlus | Neural network algorithm | - |
dc.subject.keywordPlus | Recognition accuracy | - |
dc.subject.keywordPlus | Simulation analysis | - |
dc.subject.keywordPlus | Target Classification | - |
dc.subject.keywordPlus | Target recognition | - |
dc.subject.keywordPlus | Visual recognition | - |
dc.subject.keywordPlus | Computer vision | - |
dc.subject.keywordPlus | article | - |
dc.subject.keywordPlus | artificial intelligence | - |
dc.subject.keywordPlus | convolutional neural network | - |
dc.subject.keywordPlus | deep learning | - |
dc.subject.keywordPlus | robotics | - |
dc.subject.keywordPlus | simulation | - |
dc.subject.keywordPlus | velocity | - |
dc.subject.keywordPlus | visual memory | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.