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http://dx.doi.org/10.12652/Ksce.2022.42.1.0107

Development of Deep Learning-Based Damage Detection Prototype for Concrete Bridge Condition Evaluation  

Nam, Woo-Suk (KALIS)
Jung, Hyunjun (KALIS)
Park, Kyung-Han (KALIS)
Kim, Cheol-Min (KALIS)
Kim, Gyu-Seon (KALIS)
Publication Information
KSCE Journal of Civil and Environmental Engineering Research / v.42, no.1, 2022 , pp. 107-116 More about this Journal
Abstract
Recently, research has been actively conducted on the technology of inspection facilities through image-based analysis assessment of human-inaccessible facilities. This research was conducted to study the conditions of deep learning-based imaging data on bridges and to develop an evaluation prototype program for bridges. To develop a deep learning-based bridge damage detection prototype, the Semantic Segmentation model, which enables damage detection and quantification among deep learning models, applied Mask-RCNN and constructed learning data 5,140 (including open-data) and labeling suitable for damage types. As a result of performance modeling verification, precision and reproduction rate analysis of concrete cracks, stripping/slapping, rebar exposure and paint stripping showed that the precision was 95.2 %, and the recall was 93.8 %. A 2nd performance verification was performed on onsite data of crack concrete using damage rate of bridge members.
Keywords
Deep learning; Semantic segmentation model; Mask-RCNN; Damage detection; Evaluation prototype program;
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1 Lee, Y. I., Kim, B. H. and Cho, S. J. (2018). "Image-based spalling detection of concrete structures using deep learning." Journal of the Korea Concrete Institute, Vol. 30, No. 1, pp. 91-99.   DOI
2 Li, H., Song, D., Liu, Y. and Li, B. (2017). Automatic pavement crack detection by multi-scale image fusion, TR 2017-11-1, Department of Computer Science and Engineering, Texas A&M University.
3 Amhaz, R., Chambon, S., Idier, J. and Baltazart, V. (2016). "Automatic crack detection on 2D pavement images: An algorithm based on minimal path selection." IEEE Transaction on Intelligent Transportation System, Vol. 17, No. 10, pp. 2718-2729.   DOI
4 Yang, L., Li, B., Li, W., Zhaoming, L., Yang, G. and Xiao, J. (2017a). "Deep concrete inspection using unmanned aerial vehicle towards CSSC database." 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS.
5 Cui, L., Qi, Z., Chen, Z., Meng, F. and Shi, Y. (2015). "Pavement distress detection using random decision forests." International Conference on Data Science, Springer, Cham, pp. 95-102.
6 Dorafshan, S., Maguire, M. and Qi, X. (2016). Automatic surface crack detection in concrete structures using OTSU thresholding and morphological operations, UTC Report 01-2016, Utah State University, Logan, Utah, USA.
7 Dorafshan, S., Thomas, J. R. and Maguire, M. (2018a). "Comparison of deep learning convolutional neural networks and edge detectors for image-based crack detection in concrete." Construction and Building Materials, Vol. 186, pp. 1031-1045.   DOI
8 Dorafshan, S., Thomas, R. J., Coopmans, C. and Maguire, M. (2018b). "Deep learning neural networks for sUAS-assisted structural inspections, feasibility and application." 2018 International Conference on Unmanned Aircraft Systems, ICUAS, Dallas, TX.
9 Hoskere, V., Narazaki, Y., Hoang, T. A. and Spencer Jr, B. F. (2017). Vision-based structural inspection using multiscale deep convolutional neural networks, 3rd Huixian International Forum on Earthquake Engineering for Young Researchers, August 11-12, 2017, University of Illinois, Urbana-Champaign, United States.
10 Shi, Y., Cui, L., Qi, Z., Meng, F. and Chen, Z. (2016). "Automatic road crack detection using random structured forests." IEEE Transactions on Intelligent Transportation Systems, Vol. 17, No. 12, pp. 3434-3445.   DOI
11 Yang, L., Li, B., Li, W., Liu, Z., Yang, G. and Xiao, J. (2017b). "A robotic system towards concrete structure spalling and crack database." 2017 IEEE International Conference on Robotics and\ Biomimetics, ROBIO.
12 Zhang, L., Yang, F., Zhang, Y. D. and Zhu, Y. J. (2016). "Road crack detection using deep convolutional neural network." 2016 IEEE International Conference on Image Processing, ICIP, DOI: http://doi.org/10.1109/ICIP.2016.7533052.   DOI