Target Recognition of Industrial Robots Using Machine Vision in 5G Environment
Archivos
Fecha
2021
Profesor Guía
Formato del documento
Articulo
ORCID Autor
Título de la revista
ISSN de la revista
Título del volumen
Editor
Front Neurorobot
ISBN
ISSN
1662-5218 (Print) 1662-5218
item.page.issne
item.page.doiurl
Departamento o Escuela
Determinador
Recolector
Especie
Nota general
Resumen
"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."
Descripción
Lugar de Publicación
Auspiciador
Palabras clave
5G, ENVIRONMENT, ARTIFICIAL INTELLIGENCE, DEEP LEARNING, INDUSTRIAL ROBOT, MACHINE VISION