Adoption of Convolutional Neural Network Algorithm Combined with Augmented Reality in Building Data Visualization and Intelligent Detection

It aims to improve the degree of visualization of building data, ensure the ability of intelligent detection, and effectively solve the problems encountered in building data processing. Convolutional neural network and augmented reality technology are adopted, and a building visualization model base...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2021, Vol.2021 (1)
Main Authors: Wei, Minghui, Tang, Jingjing, Tang, Haotian, Zhao, Rui, Gai, Xiaohui, Lin, Renying
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial neural networks
Augmented reality
Building construction
Buildings
Cellular telephones
Cities
Data processing
Deep learning
Efficiency
Flexibility
Mobile computing
Neural networks
Remote sensing
Scientific visualization
Target detection
Teaching
User experience
Visualization
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Summary:It aims to improve the degree of visualization of building data, ensure the ability of intelligent detection, and effectively solve the problems encountered in building data processing. Convolutional neural network and augmented reality technology are adopted, and a building visualization model based on convolutional neural network and augmented reality is proposed. The performance of the proposed algorithm is further confirmed by performance verification on public datasets. It is found that the building target detection model based on convolutional neural network and augmented reality has obvious advantages in algorithm complexity and recognition accuracy. It is 25 percent more accurate than the latest model. The model can make full use of mobile computing resources, avoid network delay and dependence, and guarantee the real-time requirement of data processing. Moreover, the model can also well realize the augmented reality navigation and interaction effect of buildings in outdoor scenes. To sum up, this study provides a research idea for the identification, data processing, and intelligent detection of urban buildings.
ISSN:1076-2787
1099-0526
DOI:10.1155/2021/5161111