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Title: Transfer learning on convolutional activation feature as applied to a building quality assessment robot
Authors: Liu, Lili
Yan, Rui-Jun
Maruvanchery, Varun
Kayacan, Erdal
Chen, I-Ming
Tiong, Lee Kong
Keywords: DRNTU::Engineering::Mechanical engineering
Active Transfer Learning
Deep Learning
Issue Date: 2017
Source: Liu, 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:10.1177/1729881417712620
Series/Report no.: International Journal of Advanced Robotic Systems
Abstract: We 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.
ISSN: 1729-8806
DOI: 10.1177/1729881417712620
Rights: © 2017 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 License ( 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 (
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:RRC Journal Articles

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