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dc.contributor.authorYing, Wenzheng
dc.contributor.supervisorXiangyu Wangen_US
dc.contributor.supervisorSong Wangen_US
dc.contributor.supervisorJunxiang Zhuen_US
dc.contributor.supervisorChangzhi Wuen_US
dc.date.accessioned2022-05-05T01:32:23Z
dc.date.available2022-05-05T01:32:23Z
dc.date.issued2021en_US
dc.identifier.urihttp://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.publisherCurtin Universityen_US
dc.titleAutomatic Scaffolding Productivity Measurement through Deep Learningen_US
dc.typeThesisen_US
dcterms.educationLevelPhDen_US
curtin.departmentSchool of Design and the Built Environmenten_US
curtin.accessStatusOpen accessen_US
curtin.facultyHumanitiesen_US
curtin.contributor.orcidYing, Wenzheng [0000-0003-1759-8769]en_US


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