JKSMI
Journal of the Korea Institute for
Structural Maintenance and Inspection
KSMI
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ISSN : 2234-6937 (Print)
ISSN : 2287-6979 (Online)
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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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2026-08
(Vol.30 No.4)
10.11112/jksmi.2026.30.4.67
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References
1
Alkannad, A. A., Al Smadi, A., Yang, S., Al-Smadi, M. K., Al-Makhlafi, M., Feng, Z., Yin, Z. (2025), CrackVision: Effective concrete crack detection with deep learning and transfer learning, IEEE Access
2
Dorafshan, S., Thomas, R. J., Maguire, M. (2018), Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete, Construction and Building Materials, 186, 1031-1045.
3
Fan, C. L. (2025), Concrete crack detection and severity assessment using deep learning and multispectral imagery analysis, Measurement, 247
4
Jamshidi, M., El-Badry, M., Nourian, N. (2023), Improving concrete crack segmentation networks through CutMix data synthesis and temporal data fusion, Sensors, 23(1), 504
5
Jeon, E. I., Jang, D. H., Park, H. S. (2025), UAV Facade Orthomosaic-Based Crack Analysis and Generation of an Exterior Damage Map for Bridges, Journal of the Korea Institute for Structural Maintenance and Inspection, 29(5), 132-140. (in Korean)
6
Kim, S. M., Sohn, J. M., Kim, D. S. (2020), A method for concrete crack detection using U-Net based image inpainting technique, Journal of the Korea Society of Computer and Information, 25(10), 35-42. (in Korean)
7
Manjunatha, P., Masri, S. F., Nakano, A., Wellford, L. C. (2024), CrackDenseLinkNet: a deep convolutional neural network for semantic segmentation of cracks on concrete surface images, Structural Health Monitoring, 23(2), 796-817.
8
Mumuni, A., Mumuni, F. (2022), Data augmentation: A comprehensive survey of modern approaches, Array, 16
9
Munawar, H. S., Hammad, A. W. A., Waller, S. T. (2021), Image-Based Crack Detection Methods: A Review, Infrastructures, 6(8), 115
10
Nnolim, U. A. (2020), Automated crack segmentation via saturation channel thresholding, area classification and fusion of modified level set segmentation with Canny edge detection, Heliyon, 6(12)
11
Roy, S., Yogi, B., Majumdar, R., Ghosh, P., Das, S. K. (2025), Deep learning-based crack detection and prediction for structural health monitoring, Discover Applied Sciences, 7(7), 674
12
Shi, Z., Jin, N., Chen, D., Ai, D. (2024), A comparison study of semantic segmentation networks for crack detection in construction materials, Construction and Building Materials, 414
13
Shim, S. B. (2023), Mean Teacher Learning Structure Optimization for Semantic Segmentation of Crack Detection, Journal of the Korea Institute for Structural Maintenance and Inspection, 27(5), 113-119. (in Korean)
14
Shim, S. B., Min, J. Y. (2022), Semantic Segmentation for Multiple Concrete Damage Based on Hierarchical Learning, Journal of the Korea Institute for Structural Maintenance and Inspection, 26(6), 175-181. (in Korean)
15
Yu, J., Xu, Y., Xing, C., Zhou, J., Pan, P. (2023), Pixel‐Level Crack Detection and Quantification of Nuclear Containment with Deep Learning, Structural Control and Health Monitoring, 2023(1)
16
Yun, S., Han, D., Oh, S. J., Chun, S., Choe, J., Yoo, Y. (2019), Cutmix: Regularization strategy to train strong classifiers with localizable features, Proceedings of the IEEE/CVF International Conference on Computer Vision, 6023-6032.
17
Zhang, H., Cisse, M., Dauphin, Y. N., Lopez-Paz, D. (2017), mixup: Beyond empirical risk minimization, arXiv preprint arXiv:1710.09412
18
Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., Liang, J. (2018), Unet++: A nested u-net architecture for medical image segmentation, International workshop on deep learning in medical image analysis, 3-11., Cham: Springer International Publishing