Vision-Based Three-Dimensional Reconstruction and Monitoring of Large-Scale Steel Tubular Structures
A four-ocular vision system is proposed for the three-dimensional (3D) reconstruction of large-scale concrete-filled steel tube (CFST) under complex testing conditions. These measurements are vitally important for evaluating the seismic performance and 3D deformation of large-scale specimens. A four...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/1236021 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849691442458394624 |
|---|---|
| author | Yunchao Tang Mingyou Chen Yunfan Lin Xueyu Huang Kuangyu Huang Yuxin He Lijuan Li |
| author_facet | Yunchao Tang Mingyou Chen Yunfan Lin Xueyu Huang Kuangyu Huang Yuxin He Lijuan Li |
| author_sort | Yunchao Tang |
| collection | DOAJ |
| description | A four-ocular vision system is proposed for the three-dimensional (3D) reconstruction of large-scale concrete-filled steel tube (CFST) under complex testing conditions. These measurements are vitally important for evaluating the seismic performance and 3D deformation of large-scale specimens. A four-ocular vision system is constructed to sample the large-scale CFST; then point cloud acquisition, point cloud filtering, and point cloud stitching algorithms are applied to obtain a 3D point cloud of the specimen surface. A point cloud correction algorithm based on geometric features and a deep learning algorithm are utilized, respectively, to correct the coordinates of the stitched point cloud. This enhances the vision measurement accuracy in complex environments and therefore yields a higher-accuracy 3D model for the purposes of real-time complex surface monitoring. The performance indicators of the two algorithms are evaluated on actual tasks. The cross-sectional diameters at specific heights in the reconstructed models are calculated and compared against laser rangefinder data to test the performance of the proposed algorithms. A visual tracking test on a CFST under cyclic loading shows that the reconstructed output well reflects the complex 3D surface after correction and meets the requirements for dynamic monitoring. The proposed methodology is applicable to complex environments featuring dynamic movement, mechanical vibration, and continuously changing features. |
| format | Article |
| id | doaj-art-e887faa276204ef08a51fb26a1029a86 |
| institution | DOAJ |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-e887faa276204ef08a51fb26a1029a862025-08-20T03:21:02ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/12360211236021Vision-Based Three-Dimensional Reconstruction and Monitoring of Large-Scale Steel Tubular StructuresYunchao Tang0Mingyou Chen1Yunfan Lin2Xueyu Huang3Kuangyu Huang4Yuxin He5Lijuan Li6College of Urban and Rural Construction, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, ChinaKey Laboratory of Key Technology on Agricultural Machine and Equipment, Collage of Engineering, South China Agricultural University, Guangzhou 510642, ChinaCollege of Urban and Rural Construction, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, ChinaCollege of Urban and Rural Construction, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, ChinaKey Laboratory of Key Technology on Agricultural Machine and Equipment, Collage of Engineering, South China Agricultural University, Guangzhou 510642, ChinaKey Laboratory of Key Technology on Agricultural Machine and Equipment, Collage of Engineering, South China Agricultural University, Guangzhou 510642, ChinaSchool of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510000, ChinaA four-ocular vision system is proposed for the three-dimensional (3D) reconstruction of large-scale concrete-filled steel tube (CFST) under complex testing conditions. These measurements are vitally important for evaluating the seismic performance and 3D deformation of large-scale specimens. A four-ocular vision system is constructed to sample the large-scale CFST; then point cloud acquisition, point cloud filtering, and point cloud stitching algorithms are applied to obtain a 3D point cloud of the specimen surface. A point cloud correction algorithm based on geometric features and a deep learning algorithm are utilized, respectively, to correct the coordinates of the stitched point cloud. This enhances the vision measurement accuracy in complex environments and therefore yields a higher-accuracy 3D model for the purposes of real-time complex surface monitoring. The performance indicators of the two algorithms are evaluated on actual tasks. The cross-sectional diameters at specific heights in the reconstructed models are calculated and compared against laser rangefinder data to test the performance of the proposed algorithms. A visual tracking test on a CFST under cyclic loading shows that the reconstructed output well reflects the complex 3D surface after correction and meets the requirements for dynamic monitoring. The proposed methodology is applicable to complex environments featuring dynamic movement, mechanical vibration, and continuously changing features.http://dx.doi.org/10.1155/2020/1236021 |
| spellingShingle | Yunchao Tang Mingyou Chen Yunfan Lin Xueyu Huang Kuangyu Huang Yuxin He Lijuan Li Vision-Based Three-Dimensional Reconstruction and Monitoring of Large-Scale Steel Tubular Structures Advances in Civil Engineering |
| title | Vision-Based Three-Dimensional Reconstruction and Monitoring of Large-Scale Steel Tubular Structures |
| title_full | Vision-Based Three-Dimensional Reconstruction and Monitoring of Large-Scale Steel Tubular Structures |
| title_fullStr | Vision-Based Three-Dimensional Reconstruction and Monitoring of Large-Scale Steel Tubular Structures |
| title_full_unstemmed | Vision-Based Three-Dimensional Reconstruction and Monitoring of Large-Scale Steel Tubular Structures |
| title_short | Vision-Based Three-Dimensional Reconstruction and Monitoring of Large-Scale Steel Tubular Structures |
| title_sort | vision based three dimensional reconstruction and monitoring of large scale steel tubular structures |
| url | http://dx.doi.org/10.1155/2020/1236021 |
| work_keys_str_mv | AT yunchaotang visionbasedthreedimensionalreconstructionandmonitoringoflargescalesteeltubularstructures AT mingyouchen visionbasedthreedimensionalreconstructionandmonitoringoflargescalesteeltubularstructures AT yunfanlin visionbasedthreedimensionalreconstructionandmonitoringoflargescalesteeltubularstructures AT xueyuhuang visionbasedthreedimensionalreconstructionandmonitoringoflargescalesteeltubularstructures AT kuangyuhuang visionbasedthreedimensionalreconstructionandmonitoringoflargescalesteeltubularstructures AT yuxinhe visionbasedthreedimensionalreconstructionandmonitoringoflargescalesteeltubularstructures AT lijuanli visionbasedthreedimensionalreconstructionandmonitoringoflargescalesteeltubularstructures |