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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2026-02
(Vol.30 No.1)
10.11112/jksmi.2026.30.1.72
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References
1
Bae, S., Kim, B., Cho, S. (2025), Crack assessment using cascade mask R-CNN and dilation-erosion processing technique, Journal of Computing in Civil Engineering, 39(5), 04025054
2
Beyene, D. A., Tran, D. Q., Maru, M. B., Kim, T., Park, S., Park, S. (2023), Unsupervised Domain Adaptation-based Crack Segmentation Using Transformer Network, Journal of Building Engineering, 80, 107889
3
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A. L. (2018), DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs, IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834-848.
4
Fan, X., Cao, P., Shi, P., Chen, X., Zhou, X., Gong, Q. (2022), An Underwater Dam Crack Image Segmentation Method Based on Multi-level Adversarial Transfer Learning, Neurocomputing, 505, 19-29.
5
Farahani, A., Voghoei, S., Arabnia, H. R., Rasheed, K. (2020), A Brief Review of Domain Adaptation, arXiv preprint, arXiv: 2010.03978
6
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y. (2014), Generative Adversarial Nets, 2672-2680.
7
He, K., Zhang, X., Ren, S., Sun, J. (2016), Deep Residual Learning for Image Recognition, 770-778.
8
Hoyer, L., Dai, D., Van Gool, L. (2022), DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation, 9924-9935.
9
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
10
Ji, A., Xue, X., Wang, Y., Luo, X., Xue, W. (2020), An Integrated Approach to Automatic Pixel-Level Crack Detection and Quantification of Asphalt Pavement, Automation in Construction, 114, 103176
11
Kingma, D. P., Ba, J. L. (2015), Adam: A Method for Stochastic Optimization
12
Li, D., Duan, Z., Hu, X., Zhang, D. (2021), Pixel-Level Recognition of Pavement Distresses Based on U-Net, Advances in Materials Science and Engineering, 2021, 5586615
13
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C. L. (2014), Microsoft COCO: Common Objects in Context, 740-755.
14
Liu, W., Huang, Y., Li, Y., Chen, Q. (2019), FPCNet: Fast Pavement Crack Detection Network Based on Encoder-Decoder Architecture, arXiv preprint, arXiv:1907.02248
15
Liu, N., Xu, X., Su, Y., Zhang, H., Li, H. C. (2025), Pointsam: Pointly-supervised segment anything model for remote sensing images, IEEE Transactions on Geoscience and Remote Sensing
16
Pak, M., Kim, S. (2021), Crack Detection Using Fully Convolutional Network In Wall-Climbing Robot, Advances in Computer Science and Ubiquitous Computing, 267-272.
17
Paul, S., Tsai, Y.-H., Schulter, S., Roy-Chowdhury, A. K., Chandraker, M. (2020), Domain Adaptive Semantic Segmentation Using Weak Labels, arXiv preprint, arXiv:2007.15176
18
Shi, Y., Cui, L., Qi, Z., Meng, F., Chen, Z. (2016), Automatic Road Crack Detection Using Random Structured Forests, IEEE Transactions on Intelligent Transportation Systems, 17(12), 3434-3445.
19
Shin, I., Kim, D. J., Cho, J. W., Woo, S., Park, K., Kweon, I. S. (2021), Labor: Labeling only if required for domain adaptive semantic segmentation, 8588-8598.
20
Tsai, Y.-H., Hung, W.-C., Schulter, S., Sohn, K., Yang, M.-H., Chandraker, M. (2018), Learning to Adapt Structured Output Space for Semantic Segmentation, 7472-7481.
21
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., Polosukhin, I. (2017), Attention Is All You Need, 5998-6008.
22
Wang, M., Deng, W. (2018), Deep Visual Domain Adaptation: A Survey, Neurocomputing, 312, 135-153.
23
Weng, X., Huang, Y., Li, Y., Yang, H., Yu, S. (2023), Unsupervised Domain Adaptation for Crack Detection, Automation in Construction, 153, 104939
24
Yang, X., Li, H., Yu, Y., Luo, X., Huang, T., Yang, X. (2018), Automatic Pixel-Level Crack Detection and Measurement Using Fully Convolutional Network, Computer-Aided Civil and Infrastructure Engineering, 33(12), 1090-1109.
25
Zhang, L., Yang, F., Zhang, Y. D., Zhu, Y. J. (2016), Road Crack Detection Using Deep Convolutional Neural Network, 3708-3712.
26
Zou, Q., Zhang, Z., Li, Q., Qi, X., Wang, Q., Wang, S. (2019), DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection, IEEE Transactions on Image Processing, 28(3), 1498-1512.