Visual Relationship-Based Identification of Key Construction Scenes on Highway Bridges
Highway bridges play an important role in traffic construction; however, accidents caused by bridge construction occur frequently, resulting in significant loss of life and property. The identification of bridge construction scenes not only keeps track of the construction progress, but also enables...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/12/6/827 |
| _version_ | 1797489285833687040 |
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| author | Chen Wang Jingguo Lv Yu Geng Yiting Liu |
| author_facet | Chen Wang Jingguo Lv Yu Geng Yiting Liu |
| author_sort | Chen Wang |
| collection | DOAJ |
| description | Highway bridges play an important role in traffic construction; however, accidents caused by bridge construction occur frequently, resulting in significant loss of life and property. The identification of bridge construction scenes not only keeps track of the construction progress, but also enables real-time monitoring of the construction process and the timely detection of safety hazards. This paper proposes a deep learning method in artificial intelligence (AI) for identifying key construction scenes of highway bridges based on visual relationships. First, based on the analysis of bridge construction characteristics and construction process, five key construction scenes are selected. Then, by studying the underlying features of the five scenes, a construction scene identification feature information table is built, and construction scene identification rules are formulated. Afterward, a bridge key construction scene identification model (CSIN) is built; this model comprises target detection, visual relationship extraction, semantic conversion, scene information fusion, and identification results output. Finally, the effectiveness of the proposed method is verified experimentally. The results show that the proposed method can effectively identify key construction scenes for highway bridges with an accuracy rate of 94%, and enable the remote intelligent monitoring of highway bridge construction processes to ensure that projects are carried out safely. |
| first_indexed | 2024-03-10T00:14:22Z |
| format | Article |
| id | doaj.art-c5a6790deb6243f1be3beb3b7de92ae1 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-10T00:14:22Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-c5a6790deb6243f1be3beb3b7de92ae12023-11-23T15:53:58ZengMDPI AGBuildings2075-53092022-06-0112682710.3390/buildings12060827Visual Relationship-Based Identification of Key Construction Scenes on Highway BridgesChen Wang0Jingguo Lv1Yu Geng2Yiting Liu3School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102612, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102612, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102612, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102612, ChinaHighway bridges play an important role in traffic construction; however, accidents caused by bridge construction occur frequently, resulting in significant loss of life and property. The identification of bridge construction scenes not only keeps track of the construction progress, but also enables real-time monitoring of the construction process and the timely detection of safety hazards. This paper proposes a deep learning method in artificial intelligence (AI) for identifying key construction scenes of highway bridges based on visual relationships. First, based on the analysis of bridge construction characteristics and construction process, five key construction scenes are selected. Then, by studying the underlying features of the five scenes, a construction scene identification feature information table is built, and construction scene identification rules are formulated. Afterward, a bridge key construction scene identification model (CSIN) is built; this model comprises target detection, visual relationship extraction, semantic conversion, scene information fusion, and identification results output. Finally, the effectiveness of the proposed method is verified experimentally. The results show that the proposed method can effectively identify key construction scenes for highway bridges with an accuracy rate of 94%, and enable the remote intelligent monitoring of highway bridge construction processes to ensure that projects are carried out safely.https://www.mdpi.com/2075-5309/12/6/827construction scene identificationvisual relationship detectionscene rulesdeep learningneural networkshighway bridges |
| spellingShingle | Chen Wang Jingguo Lv Yu Geng Yiting Liu Visual Relationship-Based Identification of Key Construction Scenes on Highway Bridges Buildings construction scene identification visual relationship detection scene rules deep learning neural networks highway bridges |
| title | Visual Relationship-Based Identification of Key Construction Scenes on Highway Bridges |
| title_full | Visual Relationship-Based Identification of Key Construction Scenes on Highway Bridges |
| title_fullStr | Visual Relationship-Based Identification of Key Construction Scenes on Highway Bridges |
| title_full_unstemmed | Visual Relationship-Based Identification of Key Construction Scenes on Highway Bridges |
| title_short | Visual Relationship-Based Identification of Key Construction Scenes on Highway Bridges |
| title_sort | visual relationship based identification of key construction scenes on highway bridges |
| topic | construction scene identification visual relationship detection scene rules deep learning neural networks highway bridges |
| url | https://www.mdpi.com/2075-5309/12/6/827 |
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