MSF-ACNN: multi-scale feature fusion atrous convolutional neural networks for pedestrian fine-grained attribution detection

2019 ◽  
Author(s):  
zhenxia Yu ◽  
miaomiao lou ◽  
lin chen
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Feature Fusion ◽  
Scale Feature ◽  
Fine Grained ◽  
Multi Scale

scholarly journals Battlefield Target Aggregation Behavior Recognition Model Based on Multi-Scale Feature Fusion

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 761 ◽  
Author(s):  
Haiyang Jiang ◽  
Yaozong Pan ◽  
Jian Zhang ◽  
Haitao Yang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Feature Fusion ◽  
Computational Cost ◽  
Aggregation Behavior ◽  
Behavior Recognition ◽  
Scale Feature ◽  
Multi Scale ◽  
Model Based ◽  
Spatio Temporal

In this paper, our goal is to improve the recognition accuracy of battlefield target aggregation behavior while maintaining the low computational cost of spatio-temporal depth neural networks. To this end, we propose a novel 3D-CNN (3D Convolutional Neural Networks) model, which extends the idea of multi-scale feature fusion to the spatio-temporal domain, and enhances the feature extraction ability of the network by combining feature maps of different convolutional layers. In order to reduce the computational complexity of the network, we further improved the multi-fiber network, and finally established an architecture—3D convolution Two-Stream model based on multi-scale feature fusion. Extensive experimental results on the simulation data show that our network significantly boosts the efficiency of existing convolutional neural networks in the aggregation behavior recognition, achieving the most advanced performance on the dataset constructed in this paper.

scholarly journals Feature Extraction and Analysis of Microseismic Signal Based on Convolutional Neural Network

2021 ◽  
Vol 2143 (1) ◽  
pp. 012017
Author(s):  
Hui Zhang ◽  
Hao Zhai ◽  
Ke Zhang ◽  
Lujun Wang ◽  
Xing Zhao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Big Data ◽  
Convolutional Neural Networks ◽  
Feature Fusion ◽  
Practical Significance ◽  
Scale Feature ◽  
Multi Scale ◽  
Seismic Detection ◽  
Microseismic Signal

Abstract Seismic detection technology has been widely used in safety detection of engineering construction abroad. Although it has just started in the field of engineering in our country, its role is becoming more and more important. Through computer technology, micro-seismic detection can provide accurate data for the construction safety detection of large-scale projects, which has important practical significance for the rapid and effective identification of micro-seismic signals. Based on this, the purpose of this article is to study the feature extraction and classification of microseismic signals based on neural games. This article first summarizes the development status of microseismic monitoring technology. Using traditional convolutional neural networks for analysis, a multi-scale feature fusion network is proposed on the basis of convolutional neural networks and big data, the multi-scale feature fusion network is used to research and analyze microseismic feature extraction and classification. This article systematically explains The principle of microseismic signal acquisition and the construction of multi-scale feature fusion network. And use big data, comparative analysis method, observation method and other research methods to study the theme of this article. Experimental research shows that the db7 wavelet base has little effect on the Megatron signal.

Multi-feature fusion of convolutional neural networks for Fine-Grained ship classification

2019 ◽  
Vol 37 (1) ◽  
pp. 125-135 ◽  
Author(s):  
Sizhe Huang ◽  
Huosheng Xu ◽  
Xuezhi Xia ◽  
Fan Yang ◽  
Fuhao Zou
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Feature Fusion ◽  
Fine Grained ◽  
Ship Classification

Fine-Grained Image Classification Based on Multi-Scale Feature Fusion

2020 ◽  
Vol 57 (12) ◽  
pp. 121002
Author(s):  
李思瑶 Li Siyao ◽  
刘宇红 Liu Yuhong ◽  
张荣芬 Zhang Rongfen
Keyword(s):  
Image Classification ◽  
Feature Fusion ◽  
Scale Feature ◽  
Fine Grained ◽  
Multi Scale
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