scholarly journals Action Recognition Based on CSI Signal Using Improved Deep Residual Network Model

2022 ◽  
Vol 130 (3) ◽  
pp. 1827-1851
Author(s):  
Jian Zhao ◽  
Shangwu Chong ◽  
Liang Huang ◽  
Xin Li ◽  
Chen He ◽  
...  
Keyword(s):  
Action Recognition ◽  
Network Model ◽  
Residual Network

Multi-cue based 3D residual network for action recognition

2020 ◽  
Author(s):  
Ming Zong ◽  
Ruili Wang ◽  
Zhe Chen ◽  
Maoli Wang ◽  
Xun Wang ◽  
...  
Keyword(s):  
Action Recognition ◽  
Residual Network

An improved residual network model for image recognition using a combination of snapshot ensembles and the cutout technique

2019 ◽  
Vol 79 (1-2) ◽  
pp. 1475-1495
Author(s):  
Chakkrit Termritthikun ◽  
Yeshi Jamtsho ◽  
Paisarn Muneesawang
Keyword(s):  
Network Model ◽  
Image Recognition ◽  
Residual Network

scholarly journals Imperfect Wheat Grain Recognition Combined with an Attention Mechanism and Residual Network

2021 ◽  
Vol 11 (11) ◽  
pp. 5139
Author(s):  
Weiwei Zhang ◽  
Huimin Ma ◽  
Xiaohong Li ◽  
Xiaoli Liu ◽  
Jun Jiao ◽  
...  
Keyword(s):  
Machine Vision ◽  
Network Model ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Attention Mechanism ◽  
Residual Network ◽  
Wheat Grains ◽  
Key Factor ◽  
Different Depths ◽  
Deep Learning Network

Intelligent detection of imperfect wheat grains based on machine vision is of great significance to correctly and rapidly evaluate wheat quality. There is little difference between the partial characteristics of imperfect and perfect wheat grains, which is a key factor limiting the classification and recognition accuracy of imperfect wheat based on a deep learning network model. In this paper, we propose a method for imperfect wheat grains recognition combined with an attention mechanism and residual network (ResNet), and verify its recognition accuracy by adding an attention mechanism module into different depths of residual network. Five residual networks with different depths (18, 34, 50, 101, and 152) were selected for the experiment, it was found that the recognition accuracy of each network model was improved with the attention mechanism, and the average recognition rate of ResNet-50 with the addition of the attention mechanism reached 96.5%. For ResNet-50 with the attention mechanism, the optimal learning rate was further screened as 0.0003. The average recognition accuracy reached 97.5%, among which the recognition rates of scab wheat grains, insect-damaged wheat grains, sprouted wheat grains, mildew wheat grains, broken wheat grains, and perfect wheat grains reached 97%, 99%, 99%, 95%, 96%, and 99% respectively. This work can provide guidance for the detection and recognition of imperfect wheat grains using machine vision.

scholarly journals Application of Convolution Neural Network (CNN) Model Combined with Pyramid Algorithm in Aerobics Action Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qi Liang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Action Recognition ◽  
Network Model ◽  
Neural Network Model ◽  
Convolution Neural Network ◽  
Recognition Method ◽  
Recognition Model ◽  
Recognition Function ◽  
The Common

In order to realize high-accuracy recognition of aerobics actions, a highly applicable deep learning model and faster data processing methods are required. Therefore, it is a major difficulty in the field of research on aerobics action recognition. Based on this, this paper studies the application of the convolution neural network (CNN) model combined with the pyramid algorithm in aerobics action recognition. Firstly, the basic architecture of the convolution neural network model based on the pyramid algorithm is proposed. Combined with the application strategy of the common recognition model in aerobics action recognition, the traditional aerobics action capture information is processed. Through the characteristics of different aerobics actions, different accurate recognition is realized, and then, the error of the recognition model is evaluated. Secondly, the composite recognition function of the convolution neural network model in this application is constructed, and the common data layer effect recognition method is used in the optimization recognition. Aiming at the shortcomings of the composite recognition function, the pyramid algorithm is used to improve the convolution neural network recognition model by deep learning optimization. Finally, through the effectiveness comparison experiment, the results show that the convolution neural network model based on the pyramid algorithm is more efficient than the conventional recognition method in aerobics action recognition.

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