Fusion of 2D CNN and 3D DenseNet for Dynamic Gesture Recognition

Zhang, Erhu; Xue, B.; Cao, Fangzhou; Duan, Jinhui; Lin, Guanghui; Lei, Yanfang

doi:10.3390/electronics8121511

Cited by 30 publications

(18 citation statements)

References 34 publications

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“…The COVID-19Net algorithm proposed in this study is parallelly connected using a 1D-CNN [ 40 ], a 2D-CNN [ 41 ], and BiGRUs to form a mixed deep learning network. Fig.…”

Section: Methodsmentioning

confidence: 99%

Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019

Huang

Shen

Kuo

et al. 2022

Socio-Economic Planning Sciences

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“…The COVID-19Net algorithm proposed in this study is parallelly connected using a 1D-CNN [ 40 ], a 2D-CNN [ 41 ], and BiGRUs to form a mixed deep learning network. Fig.…”

Section: Methodsmentioning

confidence: 99%

Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019

Huang

Shen

Kuo

et al. 2022

Socio-Economic Planning Sciences

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“…The COVID-19Net algorithm proposed in this study is parallelly connected using a 1D-CNN [32], a 2D-CNN [33], and BiGRUs to form a mixed deep learning network. Fig.…”

Section: Methodsmentioning

confidence: 99%

Novel Spatiotemporal Feature Extraction Parallel Deep Neural Network for Forecasting Confirmed Cases of Coronavirus Disease 2019

Huang

Shen

Kuo

et al. 2020

Preprint

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The coronavirus disease 2019 pandemic continues as of March 26 and spread to Europe on 10 approximately February 24. A report from April 29 revealed 1.26 million confirmed cases and 125 928 deaths 11 in Europe. This study proposed a novel deep neural network framework, COVID-19Net, which parallelly 12 combines a convolutional neural network (CNN) and bidirectional gated recurrent units (GRUs). Three 13 European countries with severe outbreaks were studied-Germany, Italy, and Spain-to extract 14 spatiotemporal feature and predict the number of confirmed cases. The prediction results acquired from 15 COVID-19Net were compared to those obtained using a CNN, GRU, and CNN-GRU. The mean absolute 16 error, mean absolute percentage error, and root mean square error, which are commonly used model 17 assessment indices, were used to compare the accuracy of the models. The results verified that COVID-19Net 18was notably more accurate than the other models. The mean absolute percentage error generated by COVID-19 19Net was 1.447 for Germany, 1.801 for Italy, and 2.828 for Spain, which were considerably lower than those 20 of the other models. This indicated that the proposed framework can accurately predict the accumulated 21 number of confirmed cases in the three countries and serve as a crucial reference for devising public health 22 strategies. 23Index Terms-Confirmed cases forecasting, COVID-19Net, parallel deep neural network 24 25

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“…e convolutions in each Dense Block are all interconnected [31]. H indicates that each input is convolved with k-dimensional 3 * 3 kernels using Batch Norm and ReLU in order to ensure that each node can output feature maps of the same dimension.…”

Section: Improved Faster R-cnnmentioning

confidence: 99%

Classification of Shellfish Recognition Based on Improved Faster R-CNN Framework of Deep Learning

Feng

Tao

Lee

2021

Mathematical Problems in Engineering

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In view of the current absence of any deep learning algorithm for shellfish identification in real contexts, an improved Faster R-CNN-based detection algorithm is proposed in this paper. It achieves multiobject recognition and localization through a second-order detection network and replaces the original feature extraction module with DenseNet, which can fuse multilevel feature information, increase network depth, and avoid the disappearance of network gradients. Meanwhile, the proposal merging strategy is improved with Soft-NMS, where an attenuation function is designed to replace the conventional NMS algorithm, thereby avoiding missed detection of adjacent or overlapping objects and enhancing the network detection accuracy under multiple objects. By constructing a real contexts shellfish dataset and conducting experimental tests on a vision recognition seafood sorting robot production line, we were able to detect the features of shellfish in different scenarios, and the detection accuracy was improved by nearly 4% compared to the original detection model, achieving a better detection accuracy. This provides favorable technical support for future quality sorting of seafood using the improved Faster R-CNN-based approach.

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Fusion of 2D CNN and 3D DenseNet for Dynamic Gesture Recognition

Cited by 30 publications

References 34 publications

Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019

Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019

Novel Spatiotemporal Feature Extraction Parallel Deep Neural Network for Forecasting Confirmed Cases of Coronavirus Disease 2019

Classification of Shellfish Recognition Based on Improved Faster R-CNN Framework of Deep Learning

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