Browse > Article
http://dx.doi.org/10.12652/Ksce.2021.41.6.0751

Image-Based Automatic Bridge Component Classification Using Deep Learning  

Cho, Munwon (Chungbuk National University)
Lee, Jae Hyuk (Chungbuk National University)
Ryu, Young-Moo (Korea Railroad Research Institute)
Park, Jeongjun (Korea Railroad Research Institute)
Yoon, Hyungchul (Chungbuk National University)
Publication Information
KSCE Journal of Civil and Environmental Engineering Research / v.41, no.6, 2021 , pp. 751-760 More about this Journal
Abstract
Most bridges in Korea are over 20 years old, and many problems linked to their deterioration are being reported. The current practice for bridge inspection mainly depends on expert evaluation, which can be subjective. Recent studies have introduced data-driven methods using building information modeling, which can be more efficient and objective, but these methods require manual procedures that consume time and money. To overcome this, this study developed an image-based automaticbridge component classification network to reduce the time and cost required for converting the visual information of bridges to a digital model. The proposed method comprises two convolutional neural networks. The first network estimates the type of the bridge based on the superstructure, and the second network classifies the bridge components. In avalidation test, the proposed system automatically classified the components of 461 bridge images with 96.6 % of accuracy. The proposed approach is expected to contribute toward current bridge maintenance practice.
Keywords
BIM; Deep learning; CNN; Bridge component classification;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Jeong, Y. S., Kim, W. S., Lee, I. K. and Lee, J. H. (2016). "Development of bridge inspection reliability and improvement strategy." Journal of the Korea Institute for Structural Maintenance and Inspection, Vol. 20, No. 5, pp. 50-57 (in Korean).   DOI
2 Jung, H. S., Lee, M. J., Yoo, M. T. and Lee, I. W. (2020). "Response dominant frequency analysis for scour safety evaluation of railroad piers." Journal of the Korean Geotechnical Society, Vol. 36, No. 11, pp. 83-95 (in Korean).   DOI
3 Krizhevsky, A., Sutskever, I. and Hinton, G. E. (2012). "Imagenet classification with deep convolutional neural networks." In Advances in Neural Information Processing Systems, Lake Tahoe, Nevada, USA, pp. 1097-1105.
4 LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P. (1998). "Gradient-based learning applied to document recognition." Proceedings of the IEEE, Vol. 86, No. 11, pp. 2278-2324.   DOI
5 Lee, T. S., Lee, J. S., Koo, J. K. and Hwang I. H. (2006). "A fundamental study on the feasibility analysis of the introduction of automated bridge inspection equipment." Proceedings of the Journal of the Korean Society of Civil Engineers, KSCE, pp. 3677-3680 (in Korean).
6 Lim, S. C., Lee, J. H., Han, S. W. and Byun, G. S. (2017). "Study on the Method of Inspection of railway bridge of Busan urban railway using drone." Proceedings of The Korean Institute of Electrical Engineers, pp. 139-140 (in Korean).
7 Meng, X., Nguyen, D. T., Owen, J. S., Xie, Y., Psimoulis, P. and Ye, G. (2019). "Application of GeoSHM system in monitoring extreme wind events at the forth road bridge." Remote Sensing, Vol. 11, No. 23, pp. 2799.   DOI
8 Calvi, G. M., Moratti, M., O'Reilly, G. J., Scattarreggia. N., Monteiro, R., Malomo, D., Calvi, P. M. and Pinho, R. (2019). "Once upon a time in Italy: The tale of the Morandi Bridge." Structural Engineering International, Vol. 29, No. 2, pp. 198-217.   DOI
9 National Assembly Reseach Service (NARS) (2019). Road maintenance status and project, No. r2019-37 (in Korean).
10 Lee, J. H., Park, J. J. and Yoon, H. C. (2020). "Automatic classification of bridge component based on deep learning." Journal of the Korean Society of Civil Engineers, KSCE, Vol. 40, No. 2, pp. 239-245 (in Korean).   DOI
11 Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. and Rabinovich, A. (2015). "Going deeper with convolutions." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, SanJuan, Puerto Rico, USA, pp. 1-9.
12 He, K., Zhang, X., Ren, S. and Sun, J. (2016). "Deep residual learning for image recognition." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, San Juan, Puerto Rico, USA, pp. 770-778.
13 Lee, G. (2016). "10 Years of BIM application in Korea in interior design." Korean Institute of Interior Design, pp. 17-20 (in Korean).
14 Oh, D. G. (2011). A study on the bim utilization of building maintenance management through case study, Ph.D. Thesis, Hanyang University (in Korean).
15 Yoon, H. C., Elanwar, H., Choi, H. J., Golparvar-Fard, M. and Spencer Jr, B. F. (2016). "Target-free approach for vision-based structural system identification using consumer-grade cameras." Structural Control and Health Monitoring, Vol. 23, No. 12, pp. 1405-1416.   DOI
16 Park, T. S. and Park, H. S. (2018). "A study on the bim status and activation of civil engineering works." Journal of the Korean Society of Civil Engineers, KSCE, Vol. 38, No. 1, pp. 133-140 (in Korean).   DOI
17 Kang, J. O. and Lee, Y. C. (2016). "Preliminary research for drone based visual-safety inspection of bridge." Proceedings of Korean Society for Geospatial Information Science, pp. 207-210 (in Korean).