Using model-driven feedback in neural network object recognition

1993 ◽  
Author(s):  
David M. Doria ◽  
Allen Gee ◽  
James D. Leonard, Jr.
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Model Driven ◽  
Network Object

A Neural Network Object Recognition System

1990 ◽  
Author(s):  
F. P. Kuhl ◽  
A. P. Reeves ◽  
R. J. Prokop
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Recognition System ◽  
Network Object

Hybrid pyramid/neural network object recognition

1994 ◽  
Author(s):  
P. Anandan ◽  
Peter J. Burt ◽  
John C. Pearson ◽  
Clay D. Spence
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Network Object

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

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