scholarly journals Research on Image Recognition System Based on Computer Deep Learning Neural Network

2021 ◽  
Vol 2066 (1) ◽  
pp. 012009
Author(s):  
Shuqiang Du
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Recognition ◽  
Recognition System ◽  
Picture Recognition ◽  
Deep Learning Neural Network ◽  
Depth Learning

Abstract The selection and extraction of image recognition by artificial means needs more complicated work, which is not conducive to the recognition and extraction of important features. Deep learning and neural network represent the iterative expansion of computer intelligent tech, and bring significant results to image recognition. Based on this, this paper first gives the concept and model of neural network, then studies the utilization of deep learning neural network in image recognition, and finally analyses the picture recognition system on account of in-depth learning neural network.

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 Deep learning based image recognition system with neural network architecture.

2020 ◽  
Vol 45 (1) ◽  
Author(s):  
Тетяна Володимирівна Холявкіна ◽  
Ярослав Олегович Резаєв ◽  
Олексій Олексійович Харченко
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Recognition ◽  
Network Architecture ◽  
Recognition System ◽  
Neural Network Architecture

scholarly journals A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 81
Author(s):  
Jianbin Xiong ◽  
Dezheng Yu ◽  
Shuangyin Liu ◽  
Lei Shu ◽  
Xiaochan Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Plant Species ◽  
Image Recognition ◽  
Recurrent Neural Network ◽  
Plant Diseases ◽  
Learning Methods ◽  
Smart Agriculture ◽  
Important Branch

Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition. Consequently, PPIR technology that is based on deep learning is becoming increasingly popular. First, this paper introduces the development and application of PPIR technology, followed by its classification and analysis. Second, it presents the theory of four types of deep learning methods and their applications in PPIR. These methods include the convolutional neural network, deep belief network, recurrent neural network, and stacked autoencoder, and they are applied to identify plant species, diagnose plant diseases, etc. Finally, the difficulties and challenges of deep learning in PPIR are discussed.

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