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Journal of the Korea Concrete Institute

J Korea Inst. Struct. Maint. Insp.
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  • Korea Citation Index (KCI)

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  3. 2025-10 (Vol.29 No.5)
  4. 10.11112/jksmi.2025.29.5.132
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References

1 
Cheng, B., Misra, I., Schwing, A. G., Kirillov, A., and Girdhar, R. (2022), Masked-attention mask transformer for universal image segmentation, Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 1290-1299.DOI
2 
Dorafshan, S., and Maguire, M. (2018), Bridge inspection: Human performance, unmanned aerial systems and automation, Journal of Civil Structural Health Monitoring, 8(3), 443-476.DOI
3 
Mora-Félix, Z. D., et al. (2024), Performance and precision analysis of 3D surface modeling through UAVs, Physica Scripta, 99(3), 035017.DOI
4 
Jang, D. H., Park, H. S., and Jeon, E. I, (2025), Exploring the Potential of Super-Resolution for Crack Analysis in UAV Facade Orthomosaics of Small Bridges, UAV-g 2025, Espoo 10-12 Sep 2025. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.DOI
5 
Jeon, E. I., et al. (2017), Comparative evaluation of commercial softwares in UAV imagery for cultural heritage recording, Spatial Information Research, 25(5), 701-712.DOI
6 
Jeon, E. I., et al. (2021), Semantic segmentation of seagrass habitat from drone imagery based on deep learning, Ecological Informatics, 66, 101430.DOI
7 
Jeon, E. I., et al. (2023), Crack detection in concrete using deep learning for underground facility safety inspection, Journal of Korean Tunnelling and Underground Space Association, 25(6), 555-567.DOI
8 
Jeong, D. M., et al. (2019), Photogrammetric crack detection method in building using unmanned aerial vehicle, Journal of the Architectural Institute of Korea Structure & Construction, 35(1), 11-19.DOI
9 
Jeong, E., Seo, J., and Wacker, J. (2020), Literature review and technical survey on bridge inspection using unmanned aerial vehicles, Journal of Performance of Constructed Facilities, 34(6), 04020113.DOI
10 
Korea Authority of Land & Infrastructure Safety (2022), Pilot study on the use of drones in facility safety inspections, Korea Authority of Land & Infrastructure Safety, OTKCRK221052 (in Korean).URL
11 
Kim, H., Lee, J., Ahn, E., Cho, S., Shin, M., and Sim, S. H. (2017), Concrete crack identification using a UAV incorporating hybrid image processing, Sensors, 17(9), 2052.DOI
12 
Koch, C., Georgieva, K., Kasireddy, V., Akinci, B., and Fieguth, P. (2015), A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure, Advanced Engineering Informatics, 29(2), 196-210.DOI
13 
Park, D., Yu, J. I., and You, H. (2023), A research on applicability of drone photogrammetry for dam safety inspection, Journal of the Korea institute for structural maintenance and inspection, 27(5), 30-39 (in Korean).DOI
14 
Wang, H., Li, Y., Dang, L. M., Lee, S., and Moon, H. (2021a), Pixel-level tunnel crack segmentation using a weakly supervised annotation approach, Computers in Industry, 133, 103545.DOI
15 
Wang, X., Xie, L., Dong, C., and Shan, Y. (2021b), Real-esrgan: Training real-world blind super-resolution with pure synthetic data, Proceedings of the IEEE/CVF international conference on computer vision, 1905-1914.DOI
16 
Wang, W., Dai, J., Chen, Z., Huang, Z., Li, Z., Zhu, X., Hu, X., Lu, L., Li, H., Wang, X., and Qiao, Y. (2023), Internimage: Exploring large-scale vision foundation models with deformable convolutions, Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 14408-14419.DOI
17 
Yeum, C. M., and Dyke, S. J. (2015), Vision‐based automated crack detection for bridge inspection, Computer Aided Civil and Infrastructure Engineering, 30(10), 759-770.DOI
18 
Yuan, J., Ren, Q., Jia, C., Zhang, J., Fu, J., and Li, M. (2024), Automated pixel-level crack detection and quantification using deep convolutional neural networks for structural condition assessment, Structures, 59, 105780.DOI
19 
Zhao, S., Kang, F., and Li, J. (2024), Intelligent segmentation method for blurred cracks and 3D mapping of width nephograms in concrete dams using UAV photogrammetry, Automation in Construction, 157, 105145.DOI