Automatic Scaffolding Productivity Measurement through Deep Learning
| dc.contributor.author | Ying, Wenzheng | |
| dc.contributor.supervisor | Xiangyu Wang | en_US |
| dc.contributor.supervisor | Song Wang | en_US |
| dc.contributor.supervisor | Junxiang Zhu | en_US |
| dc.contributor.supervisor | Changzhi Wu | en_US |
| dc.date.accessioned | 2022-05-05T01:32:23Z | |
| dc.date.available | 2022-05-05T01:32:23Z | |
| dc.date.issued | 2021 | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.11937/88385 | |
| dc.description.abstract |
This study developed a method to automatically measure scaffolding productivity by extracting and analysing semantic information from onsite vision data. | en_US |
| dc.publisher | Curtin University | en_US |
| dc.title | Automatic Scaffolding Productivity Measurement through Deep Learning | en_US |
| dc.type | Thesis | en_US |
| dcterms.educationLevel | PhD | en_US |
| curtin.department | School of Design and the Built Environment | en_US |
| curtin.accessStatus | Open access | en_US |
| curtin.faculty | Humanities | en_US |
| curtin.contributor.orcid | Ying, Wenzheng [0000-0003-1759-8769] | en_US |