scholarly journals Performance Analysis of the Modified-Hybrid Optical Neural Network Object Recognition System Within Cluttered Scenes

10.5772/45797 ◽  
2012 ◽  
Author(s):  
Ioannis Kypraios
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Performance Analysis ◽  
Recognition System ◽  
Optical Neural Network ◽  
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

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.

scholarly journals Proposing an Image Enhancement Algorithm Using CNN for Applications of Face Recognition System

2020 ◽  
pp. 1-14
Author(s):  
Phat Nguyen Huu ◽  
Loc Hoang Bao ◽  
Hoang Lai The
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Face Recognition ◽  
Object Recognition ◽  
Facial Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Extreme Weather Events ◽  
Face Recognition System

Many researches have been going on since last two decades for object recognition, shape matching, and pattern recognition in the field of computer vision. Face recognition is one of the important issues in object recognition and computer vision. Many face image datasets, related competitions, and evaluation programs have encouraged innovation, producing more powerful facial recognition technology with promising results. In recent years, we have witnessed tremendous improvements in face recognition performance from complex deep neural network architectures trained on millions of face images. Face recognition is the most important biometric and stills many challenges such as pose variation, illumination variation, etc. In order to achieve the desired performance when deploying in reality, the methods depend on many factors. One of the main factors is quality of input image. Therefore, facial recognition systems is installed outdoors which are always affected by extreme weather events such as haze, fog. The existence of haze dramatically degrades the visibility of outdoor images captured in inclement weather and affects many high-level computer vision tasks such as detection and recognition system. In this paper, we propose a preprocessing method to remove haze from input images that enhances their quality to improve effectiveness and recognition rate for face identification based on Convolutional Neural Network (CNN) based on the available datasets and our self-built data. To perform the proposed method for outdoor face recognition system, we have improved the system accuracy from 90.53% to 98.14%. The results show that the proposed method improves the quality of the image with other traditional methods.

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
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