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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. 2025-12 (Vol.29 No.6)
  4. 10.11112/jksmi.2025.29.6.199
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References

1 
Hwang, J. H., An, S. S. (2008), An Improvement of the State Assessment for Concrete Floor Slab by Damage Type Breakdown, Journal of The Korea Institute for Structural Maintenance and Inspection, 12(2), 139-148.Google Search
2 
Ha, M. H., Park, J. S. (2010), Estimation of National Loss Expenses to Insufficient Safety Inspection and Precision Safety Diagnosis, Journal of The Korea Institute for Structural Maintenance and Inspection, 14(6), 246-253.Google Search
3 
Park, J. W. (2021), Introduction to Evaluation System on Facilities Safety Inspection and Flux activity to prevent Bad Safety Inspection for Apartment Buildings, Journal of The Korea Institute for Structural Maintenance and Inspection, 25(4), 64-71.Google Search
4 
Hwang, S. J., Kim, J. H., Yoon, J. H., Lee, S. H. (2022), A Study on the Problems of Exterior Safety Inspection of Facilities Using Drone, Proceedings of the 2022 Architectural Institute of Korea Fall Conference, 42(2), 658Google Search
5 
Zhuang, H., Cheng, Y., Zhou, M., Yang, Z. (2025), Deep learning for surface crack detection in civil engineering: A comprehensive review, Measurement, 116908DOI
6 
He, K., Zhang, X., Ren, S., Sun, J. (2016), Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778.Google Search
7 
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K. Q. (2017), Densely connected convolutional networks, Proceedings of the IEEE conference on computer vision and pattern recognition, 4700-4708.Google Search
8 
Redmon, J., Divvala, S., Girshick, R., Farhadi, A. (2016), You only look once: Unified, real-time object detection, In Proceedings of the IEEE conference on computer vision and pattern recognition, 779-788.Google Search
9 
Ren, S., He, K., Girshick, R., Sun, J. (2015), Faster r-cnn: Towards real-time object detection with region proposal networks, Advances in neural information processing systems, 28Google Search
10 
Phung, M., Duong, T. H. D., Quang, P. H. (2019), System architecture for real-time surface inspection using multiple UAVs, IEEE Systems Journal, 14(2), 2925-2936.DOI
11 
Jiang, S., Zhang, J. (2020), Real-time crack assessment using deep neural networks with wall-climbing unmanned aerial system, Computer-Aided Civil and Infrastructure Engineering, 35, 549-564.DOI
12 
Howard, A. G. (2017), Mobilenets: Efficient convolutional neural networks for mobile vision applications, arXiv preprint arXiv:1704.04861DOI
13 
Tan, M., Le, Q. (2019), Efficientnet: Rethinking model scaling for convolutional neural networks, In International Conference on Machine Learning, 6105-6114.Google Search
14 
Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Chen, J. (2024), Detrs beat yolos on real-time object detection, In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 16965-16974.Google Search
15 
Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Zitnick, C. L. (2014), Microsoft coco: Common objects in context, In European Conference on Computer Vision, 740-755.Google Search
16 
Everingham, M., Van Gool, L., Williams, C. K., Winn, J., Zisserman, A. (2010), The pascal visual object classes (voc) challenge, International journal of computer vision, 88(2), 303-338.DOI
17 
Abdel-Qader, I., Abudayyeh, O., Kelly, M. E. (2003), Analysis of edge-detection techniques for crack identification in bridges, Journal of Computing in Civil Engineering, 17(4), 255-263.DOI
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.DOI
19 
Cha, Y. J., Choi, W., Büyüköztürk, O. (2017), Deep learning‐based crack damage detection using convolutional neural networks, Computer‐Aided Civil and Infrastructure Engineering, 32(5), 361-378.Google Search
20 
Philip, R. E., Andrushia, A. D., Nammalvar, A., Gurupatham, B. G. A., Roy, K. (2023), A comparative study on crack detection in concrete walls using transfer learning techniques, Journal of Composites Science, 7(4), 169DOI
21 
Golding, V. P., Gharineiat, Z., Munawar, H. S., Ullah, F. (2022), Crack detection in concrete structures using deep learning, Sustainability, 14(13), 8117DOI
22 
Ali, L. (2021), Performance evaluation of deep cnn-based crack detection and localization techniques for concrete structures, Sensors, 21, 1688DOI
23 
Sekar, A., Perumal, V. (2021), Automatic road crack detection and classification using multi-tasking faster RCNN, Journal of Intelligent & Fuzzy Systems, 41(6), 6615-6628.DOI
24 
Yu, Z., Shen, Y., Shen, C. (2021), A real-time detection approach for bridge cracks based on YOLOv4-FPM, Automation in Construction, 122, 103514DOI
25 
Shahin, M., Chen, F. F., Maghanaki, M., Hosseinzadeh, A., Zand, N., Khodadadi Koodiani, H. (2024), Improving the concrete crack detection process via a hybrid visual transformer algorithm, Sensors, 24(10), 3247DOI
26 
(2023), Public crack detection dataset, RoboflowURL