Target Recognition of Industrial Robots Using Machine Vision in 5G Environment
- Authors
- Jin, Z.; Liu, L.; Gong, D.; Li, L.
- Issue Date
- 25-Feb-2021
- Publisher
- Frontiers Media S.A.
- Keywords
- 5G environment; artificial intelligence; deep learning; industrial robot; machine vision
- Citation
- Frontiers in Neurorobotics, v.15
- Journal Title
- Frontiers in Neurorobotics
- Volume
- 15
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/80699
- DOI
- 10.3389/fnbot.2021.624466
- ISSN
- 1662-5218
- 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - ETC > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/80699)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.