dc.contributor.authorLiu, Lili
dc.contributor.authorYan, Rui-Jun
dc.contributor.authorMaruvanchery, Varun
dc.contributor.authorKayacan, Erdal
dc.contributor.authorChen, I-Ming
dc.contributor.authorTiong, Lee Kong
dc.date.accessioned2018-09-06T06:54:07Z
dc.date.available2018-09-06T06:54:07Z
dc.date.issued2017
dc.identifier.citationLiu, L., Yan, R.-J., Maruvanchery, V., Kayacan, E., Chen, I.-M., & Tiong, L. K. (2017). Transfer learning on convolutional activation feature as applied to a building quality assessment robot. International Journal of Advanced Robotic Systems, 14(3), 1-12. doi:10.1177/1729881417712620en_US
dc.identifier.issn1729-8806en_US
dc.identifier.urihttp://hdl.handle.net/10220/45865
dc.description.abstractWe propose an automated postconstruction quality assessment robot system for crack, hollowness, and finishing defects in light of a need to speed up the inspection work, a more reliable inspection report, as well as an objective through fully automated inspection. Such an autonomous inspection system has a potential to cut labour cost significantly and achieve better accuracy. In the proposed system, a transfer learning network is employed for visual defect detection; a region proposal network is used for object region proposal, a deep learning network employed as feature extractor, and a linear classifier with supervised learning as object classifier; moreover, active learning of top-N ranking region of interest is undertaken for fine-tuning of the transfer learning on convolutional activation feature network. Extensive experiments are validated in a construction quality assessment system room and constructed test bed. The results are promising in a way that the novel proposed automated assessment method gives satisfactory results for crack, hollowness, and finishing defects assessment. To the best of our knowledge, this study is the first attempt to having an autonomous visual inspection system for postconstruction quality assessment of building sector. We believe the proposed system is going to help to pave the way towards fully autonomous postconstruction quality assessment systems in the future.en_US
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en_US
dc.format.extent12 p.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesInternational Journal of Advanced Robotic Systemsen_US
dc.rights© 2017 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).en_US
dc.subjectActive Transfer Learningen_US
dc.subjectDeep Learningen_US
dc.subjectDRNTU::Engineering::Mechanical engineeringen_US
dc.titleTransfer learning on convolutional activation feature as applied to a building quality assessment roboten_US
dc.contributor.researchRobotics Research Centreen_US
dc.identifier.doihttp://dx.doi.org/10.1177/1729881417712620
dc.description.versionPublished versionen_US


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