Feature-Based Deep Learning Classification for Pipeline Component Extraction from 3D Point Clouds
This paper proposes a novel method for construction component classification by designing a feature-based deep learning network to tackle the automation problem in construction digitization. Although scholars have proposed a variety of ways to achieve the use of deep learning to classify point cloud...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/12/7/968 |
| _version_ | 1797433752076419072 |
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| author | Zhao Xu Rui Kang Heng Li |
| author_facet | Zhao Xu Rui Kang Heng Li |
| author_sort | Zhao Xu |
| collection | DOAJ |
| description | This paper proposes a novel method for construction component classification by designing a feature-based deep learning network to tackle the automation problem in construction digitization. Although scholars have proposed a variety of ways to achieve the use of deep learning to classify point clouds, there are few practical engineering applications in the construction industry. However, in the process of building digitization, the level of manual participation has significantly reduced the efficiency of digitization and increased the application restrictions. To address this problem, we propose a robust classification method using deep learning networks, which is combined with traditional shape features for the point cloud of construction components. The proposed method starts with local and global feature extraction, where global features processed by the neural network and the traditional shape features are processed separately. Then, we generate a feature map and perform deep convolution to achieve feature fusion. Finally, experiments are designed to prove the efficiency of the proposed method based on the construction dataset we establish. This paper fills in the lack of deep learning applications of point clouds in construction component classification. Additionally, this paper provides a feasible solution to improve the construction digitization efficiency and provides an available dataset for future work. |
| first_indexed | 2024-03-09T10:21:29Z |
| format | Article |
| id | doaj.art-ae5896fbf319411b9230f105dfec3cf9 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-09T10:21:29Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-ae5896fbf319411b9230f105dfec3cf92023-12-01T21:58:27ZengMDPI AGBuildings2075-53092022-07-0112796810.3390/buildings12070968Feature-Based Deep Learning Classification for Pipeline Component Extraction from 3D Point CloudsZhao Xu0Rui Kang1Heng Li2Department of Civil Engineering, Southeast University, Nanjing 210096, ChinaDepartment of Civil Engineering, Southeast University, Nanjing 210096, ChinaDepartment of Building and Real Estate, Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, ChinaThis paper proposes a novel method for construction component classification by designing a feature-based deep learning network to tackle the automation problem in construction digitization. Although scholars have proposed a variety of ways to achieve the use of deep learning to classify point clouds, there are few practical engineering applications in the construction industry. However, in the process of building digitization, the level of manual participation has significantly reduced the efficiency of digitization and increased the application restrictions. To address this problem, we propose a robust classification method using deep learning networks, which is combined with traditional shape features for the point cloud of construction components. The proposed method starts with local and global feature extraction, where global features processed by the neural network and the traditional shape features are processed separately. Then, we generate a feature map and perform deep convolution to achieve feature fusion. Finally, experiments are designed to prove the efficiency of the proposed method based on the construction dataset we establish. This paper fills in the lack of deep learning applications of point clouds in construction component classification. Additionally, this paper provides a feasible solution to improve the construction digitization efficiency and provides an available dataset for future work.https://www.mdpi.com/2075-5309/12/7/968deep learningpipeline component extractionpoint cloudsfeatureCNN (convolutional neural network) |
| spellingShingle | Zhao Xu Rui Kang Heng Li Feature-Based Deep Learning Classification for Pipeline Component Extraction from 3D Point Clouds Buildings deep learning pipeline component extraction point clouds feature CNN (convolutional neural network) |
| title | Feature-Based Deep Learning Classification for Pipeline Component Extraction from 3D Point Clouds |
| title_full | Feature-Based Deep Learning Classification for Pipeline Component Extraction from 3D Point Clouds |
| title_fullStr | Feature-Based Deep Learning Classification for Pipeline Component Extraction from 3D Point Clouds |
| title_full_unstemmed | Feature-Based Deep Learning Classification for Pipeline Component Extraction from 3D Point Clouds |
| title_short | Feature-Based Deep Learning Classification for Pipeline Component Extraction from 3D Point Clouds |
| title_sort | feature based deep learning classification for pipeline component extraction from 3d point clouds |
| topic | deep learning pipeline component extraction point clouds feature CNN (convolutional neural network) |
| url | https://www.mdpi.com/2075-5309/12/7/968 |
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