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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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5161111 |
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| _version_ | 1832561483181981696 |
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| author | Minghui Wei Jingjing Tang Haotian Tang Rui Zhao Xiaohui Gai Renying Lin |
| author_facet | Minghui Wei Jingjing Tang Haotian Tang Rui Zhao Xiaohui Gai Renying Lin |
| author_sort | Minghui Wei |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2962784b295a4de1819a8c4d9d7a290f |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-2962784b295a4de1819a8c4d9d7a290f2025-02-03T01:24:49ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/51611115161111Adoption of Convolutional Neural Network Algorithm Combined with Augmented Reality in Building Data Visualization and Intelligent DetectionMinghui Wei0Jingjing Tang1Haotian Tang2Rui Zhao3Xiaohui Gai4Renying Lin5University of Technology Sydney, Sydney 2007, AustraliaUniversity of Technology Sydney, Sydney 2007, AustraliaUniversity of Technology Sydney, Sydney 2007, AustraliaUniversity of Technology Sydney, Sydney 2007, AustraliaUniversity of Technology Sydney, Sydney 2007, AustraliaUniversity of Sydney, Sydney 2006, AustraliaIt 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.http://dx.doi.org/10.1155/2021/5161111 |
| spellingShingle | Minghui Wei Jingjing Tang Haotian Tang Rui Zhao Xiaohui Gai Renying Lin Adoption of Convolutional Neural Network Algorithm Combined with Augmented Reality in Building Data Visualization and Intelligent Detection Complexity |
| title | Adoption of Convolutional Neural Network Algorithm Combined with Augmented Reality in Building Data Visualization and Intelligent Detection |
| title_full | Adoption of Convolutional Neural Network Algorithm Combined with Augmented Reality in Building Data Visualization and Intelligent Detection |
| title_fullStr | Adoption of Convolutional Neural Network Algorithm Combined with Augmented Reality in Building Data Visualization and Intelligent Detection |
| title_full_unstemmed | Adoption of Convolutional Neural Network Algorithm Combined with Augmented Reality in Building Data Visualization and Intelligent Detection |
| title_short | Adoption of Convolutional Neural Network Algorithm Combined with Augmented Reality in Building Data Visualization and Intelligent Detection |
| title_sort | adoption of convolutional neural network algorithm combined with augmented reality in building data visualization and intelligent detection |
| url | http://dx.doi.org/10.1155/2021/5161111 |
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