Transfer learning on convolutional activation feature as applied to a building quality assessment robot
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 syste...
| Main Authors: | , , , , , |
|---|---|
| Other Authors: | |
| Language: | English |
| Published: |
2018
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/88642 http://hdl.handle.net/10220/45865 |
| _version_ | 1811695737884377088 |
|---|---|
| author | Liu, Lili Yan, Rui-Jun Maruvanchery, Varun Kayacan, Erdal Chen, I-Ming Tiong, Lee Kong |
| author2 | Robotics Research Centre |
| author_facet | Robotics Research Centre Liu, Lili Yan, Rui-Jun Maruvanchery, Varun Kayacan, Erdal Chen, I-Ming Tiong, Lee Kong |
| author_sort | Liu, Lili |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T07:28:14Z |
| id | ntu-10356/88642 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:28:14Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/886422020-09-26T22:05:12Z Transfer learning on convolutional activation feature as applied to a building quality assessment robot Liu, Lili Yan, Rui-Jun Maruvanchery, Varun Kayacan, Erdal Chen, I-Ming Tiong, Lee Kong Robotics Research Centre DRNTU::Engineering::Mechanical engineering Active Transfer Learning Deep Learning 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. NRF (Natl Research Foundation, S’pore) Published version 2018-09-06T06:54:07Z 2019-12-06T17:07:52Z 2018-09-06T06:54:07Z 2019-12-06T17:07:52Z 2017 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 1729-8806 https://hdl.handle.net/10356/88642 http://hdl.handle.net/10220/45865 10.1177/1729881417712620 en International Journal of Advanced Robotic Systems © 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). 12 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Mechanical engineering Active Transfer Learning Deep Learning Liu, Lili Yan, Rui-Jun Maruvanchery, Varun Kayacan, Erdal Chen, I-Ming Tiong, Lee Kong Transfer learning on convolutional activation feature as applied to a building quality assessment robot |
| title | Transfer learning on convolutional activation feature as applied to a building quality assessment robot |
| title_full | Transfer learning on convolutional activation feature as applied to a building quality assessment robot |
| title_fullStr | Transfer learning on convolutional activation feature as applied to a building quality assessment robot |
| title_full_unstemmed | Transfer learning on convolutional activation feature as applied to a building quality assessment robot |
| title_short | Transfer learning on convolutional activation feature as applied to a building quality assessment robot |
| title_sort | transfer learning on convolutional activation feature as applied to a building quality assessment robot |
| topic | DRNTU::Engineering::Mechanical engineering Active Transfer Learning Deep Learning |
| url | https://hdl.handle.net/10356/88642 http://hdl.handle.net/10220/45865 |
| work_keys_str_mv | AT liulili transferlearningonconvolutionalactivationfeatureasappliedtoabuildingqualityassessmentrobot AT yanruijun transferlearningonconvolutionalactivationfeatureasappliedtoabuildingqualityassessmentrobot AT maruvancheryvarun transferlearningonconvolutionalactivationfeatureasappliedtoabuildingqualityassessmentrobot AT kayacanerdal transferlearningonconvolutionalactivationfeatureasappliedtoabuildingqualityassessmentrobot AT cheniming transferlearningonconvolutionalactivationfeatureasappliedtoabuildingqualityassessmentrobot AT tiongleekong transferlearningonconvolutionalactivationfeatureasappliedtoabuildingqualityassessmentrobot |