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- Publisher Website: 10.1016/j.autcon.2021.103970
- Scopus: eid_2-s2.0-85115891180
- WOS: WOS:000707841000003
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Article: Extracting water channels from aerial videos based on image-to-BIM registration and spatio-temporal continuity
Title | Extracting water channels from aerial videos based on image-to-BIM registration and spatio-temporal continuity |
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Authors | |
Keywords | Building information model (BIM) Computer vision Image-to-BIM registration Region of interest Unmanned aerial vehicle (UAV) Water supply infrastructure |
Issue Date | 2021 |
Citation | Automation in Construction, 2021, v. 132, article no. 103970 How to Cite? |
Abstract | Unmanned aerial vehicles (UAV) are increasingly being used in water supply infrastructure inspection, resulting in a large volume of aerial visual assets (static photographs or videos). How to efficiently extract water channels from such assets for the benefits of hazard detection remains a challenge. This paper provides an aerial video processing approach for the extraction of various water channel structures, which includes a main algorithm based on the registration of video frames into a building information model (BIM), and a complementary algorithm enabled by aerial video spatio-temporal continuity. Experimental results demonstrate the effectiveness of the proposed approach, which can reliably extract water channels with least manual intervention. The results reveal the promise of exploiting readily-available information from BIM to automate aerial video processing. Future research is suggested to further explore the generalizability of the approach concerning variant flight settings and external environments. |
Persistent Identifier | http://hdl.handle.net/10722/324191 |
ISSN | 2023 Impact Factor: 9.6 2023 SCImago Journal Rankings: 2.626 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Chen, Junjie | - |
dc.contributor.author | Liu, Donghai | - |
dc.contributor.author | Li, Xin | - |
dc.date.accessioned | 2023-01-13T03:02:07Z | - |
dc.date.available | 2023-01-13T03:02:07Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Automation in Construction, 2021, v. 132, article no. 103970 | - |
dc.identifier.issn | 0926-5805 | - |
dc.identifier.uri | http://hdl.handle.net/10722/324191 | - |
dc.description.abstract | Unmanned aerial vehicles (UAV) are increasingly being used in water supply infrastructure inspection, resulting in a large volume of aerial visual assets (static photographs or videos). How to efficiently extract water channels from such assets for the benefits of hazard detection remains a challenge. This paper provides an aerial video processing approach for the extraction of various water channel structures, which includes a main algorithm based on the registration of video frames into a building information model (BIM), and a complementary algorithm enabled by aerial video spatio-temporal continuity. Experimental results demonstrate the effectiveness of the proposed approach, which can reliably extract water channels with least manual intervention. The results reveal the promise of exploiting readily-available information from BIM to automate aerial video processing. Future research is suggested to further explore the generalizability of the approach concerning variant flight settings and external environments. | - |
dc.language | eng | - |
dc.relation.ispartof | Automation in Construction | - |
dc.subject | Building information model (BIM) | - |
dc.subject | Computer vision | - |
dc.subject | Image-to-BIM registration | - |
dc.subject | Region of interest | - |
dc.subject | Unmanned aerial vehicle (UAV) | - |
dc.subject | Water supply infrastructure | - |
dc.title | Extracting water channels from aerial videos based on image-to-BIM registration and spatio-temporal continuity | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.autcon.2021.103970 | - |
dc.identifier.scopus | eid_2-s2.0-85115891180 | - |
dc.identifier.volume | 132 | - |
dc.identifier.spage | article no. 103970 | - |
dc.identifier.epage | article no. 103970 | - |
dc.identifier.isi | WOS:000707841000003 | - |