Journal of the Korean Society of Mineral and Energy Resources Engineers ISSN:2288-0291(Print) 2288-2790(Online) 한국자원공학회지

Aela, P., Chi, H.L., Fares, A., Zayed, T., and Kim, M., 2024. UAV-based studies in railway infrastructure monitoring, Automation in Construction, 167, 105714.

Ameli, Z., Nesheli, S.J., and Landis, E.N., 2023. Deep learning-based steel bridge corrosion segmentation and condition rating using Mask R-CNN and YOLOv8, Infrastructures, 9(1), 3.

Bao, W., Du, X., Wang, N., Yuan, M., and Yang, X., 2022. A defect detection method based on BC-YOLO for transmission line components in UAV remote sensing images, Remote Sensing, 14(20), 5176.

Barreiro, A.C., Seibold, C., Hilsmann, A., and Eisert, P., 2021. Automated damage inspection of power transmission towers from UAV images, arXiv preprint arXiv:2111.15581, Cornell University, Ithaca, NY, USA.

Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L., 2017. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs, IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), p.834-848.

Chen, Q., Wen, X., Lu, S., and Sun, D., 2019. Corrosion detection for large steel structure base on UAV integrated with image processing system, Proceedings of the IOP Conference Series: Materials Science and Engineering, IOP Publishing, Jeju, Korea, 012020.

De Arriba López, V., Maboudi, M., Achanccaray, P., and Gerke, M., 2024. Automatic non-destructive UAV-based structural health monitoring of steel container cranes, Applied Geomatics, 16(1), p.125-145.

Feroz, S. and Abu Dabous, S., 2021. UAV-based remote sensing applications for bridge condition assessment, Remote Sensing, 13(9), 1809.

Forkan, A.R.M., Kang, Y.B., Jayaraman, P.P., Liao, K., Kaul, R., Morgan, G., and Sinha, S., 2022. CorrDetector: A framework for structural corrosion detection from drone images using ensemble deep learning, Expert Systems with Applications, 193, 116461.

He, K., Gkioxari, G., Dollár, P., and Girshick, R., 2017. Mask R-CNN, Proceedings of the IEEE International Conference on Computer Vision (ICCV), IEEE, Venice, Italy, p.2961-2969.

He, K., Zhang, X., Ren, S., and Sun, J., 2016. Deep residual learning for image recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Las Vegas, Nevada, USA, p.770-778.

Heo, S.J. and Na, W.S., 2025. Review of drone-based technologies for wind turbine blade inspection, Electronics, 14(2), 227.

Jiao, X., Wu, N., Zhang, X., Fan, J., Cai, Z., Wang, Y., and Zhou, Z., 2024. Enhancing tower crane safety: A UAV-based intelligent inspection approach, Buildings, 14(5), 1420.

Kim, H., Sim, S.H., and Cho, S., 2015. Unmanned aerial vehicle (UAV)-powered concrete crack detection based on digital image processing, Proceedings of the International Conference on Advances in Experimental Structural Engineering, EUCENTRE, Pavia, Italy.

Liang, H., Lee, S.C., and Seo, S., 2023. UAV-based low altitude remote sensing for concrete bridge multi-category damage automatic detection system, Drones, 7(6), 386.

Lin, J.J., Ibrahim, A., Sarwade, S., and Golparvar-Fard, M., 2021. Bridge inspection with aerial robots: Automating the entire pipeline of visual data capture, 3D mapping, defect detection, analysis, and reporting, Journal of Computing in Civil Engineering, 35(2), 04020064.

Ly, K.K. and Phung, M.D., 2020. Built infrastructure monitoring and inspection using UAVs and vision-based algorithms, arXiv preprint arXiv:2005.09486, Cornell University, Ithaca, NY, USA.

Ma, Y., Zeng, Z., Luo, Z., Tao, N., Deng, L., and Tian, Y., 2025. Current challenges and advancements of aerial thermography for outdoor structural health monitoring: A review, IEEE Sensors Journal, 25(12), p.21000-21016.

Mahajan, G., 2021. Applications of drone technology in construction industry: a study 2012-2021, International Journal of Engineering and Advanced Technology, 11(1), p.224-239.

Marchewka, A., Ziółkowski, P., and Aguilar-Vidal, V., 2020. Framework for structural health monitoring of steel bridges by computer vision, Sensors, 20(3), 700.

Nooralishahi, P., Ramos, G., Pozzer, S., Ibarra-Castanedo, C., Lopez, F., and Maldague, X.P., 2022. Texture analysis to enhance drone-based multi-modal inspection of structures, Drones, 6(12), 407.

Paik, S.H., Choi, D., Kim, Y.K., Jung, S., and Kim, D.N., 2021. Implementation of the drones with deep-learning crack detection analysis for inspection of bridge, The Journal of Korean Institute of Information Technology, 19(3), p.45-52.

Panigati, T., Zini, M., Striccoli, D., Giordano, P.F., Tonelli, D., Limongelli, M.P., and Zonta, D., 2025. Drone-based bridge inspections: Current practices and future directions, Automation in Construction, 173, 106101.

Panigrahy, S. and Karmakar, S., 2024. Real-time condition monitoring of transmission line insulators using the YOLO object detection model with a UAV, IEEE Transactions on Instrumentation and Measurement, 73, p.1-9.

Raja, A., Njilla, L., and Yuan, J., 2021. Blur the eyes of UAV: Effective attacks on UAV-based infrastructure inspection, Proceedings of the 33rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), IEEE, Washington, DC, USA, p.661-665.

Redmon, J., Divvala, S., Girshick, R., and Farhadi, A., 2016. You only look once: Unified, real-time object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Las Vegas, Nevada, USA, p.779-788.

Ren, S., He, K., Girshick, R., and Sun, J., 2015. Faster R-CNN: Towards real-time object detection with region proposal networks, Proceedings of the 28th International Conference on Neural Information Processing Systems (NeurIPS), MIT Press, Montreal, Canada

Ribeiro, D., Santos, R., Shibasaki, A., Montenegro, P., Carvalho, H., and Calçada, R., 2020. Remote inspection of RC structures using unmanned aerial vehicles and heuristic image processing, Engineering Failure Analysis, 117, 104813.

Ronneberger, O., Fischer, P., and Brox, T., 2015. U-net: Convolutional networks for biomedical image segmentation, Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, Cham, Switzerland, p.234-241.

Savino, P., Graglia, F., Scozza, G., and Di Pietra, V., 2025. Automated corrosion surface quantification in steel transmission towers using UAV photogrammetry and deep convolutional neural networks, Computer-Aided Civil and Infrastructure Engineering, 40(14), p.2050-2070.

Sikora, T., Markovic, L., and Bogdan, S., 2023. Towards operating wind turbine inspections using a lidar-equipped UAV, arXiv preprint arXiv:2306.14637, Cornell University, Ithaca, NY, USA.

Simonyan, K. and Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556, Cornell University, Ithaca, NY, USA.

The Business Research Company, 2025. Drone Inspection And Monitoring Market Report 2025: Size and Overview, Outlook, TBRC Report, London, UK.

Wang, C., Chen, G., Huang, M., and Lin, J., 2020. Rust defect detection and segmentation method for tower crane, Proceedings of the 2020 Cross Strait Radio Science & Wireless Technology Conference (CSRSWTC), IEEE, Fuzhou, China, p.1-3.

Wang, Z.F., Yu, Y.F., Wang, J., Zhang, J.Q., Zhu, H.L., Li, P., and Chen, J.P., 2022. Convolutional neural-network-based automatic dam-surface seepage defect identification from thermograms collected from UAV-mounted thermal imaging camera, Construction and Building Materials, 323, 126416.

Yeum, C.M. and Dyke, S.J., 2015. Vision-based automated crack detection for bridge inspection, Computer-Aided Civil and Infrastructure Engineering, 30(10), p.759-770.

Zhou, Q., Ding, S., Feng, Y., Wang, H., Qing, G., and Hu, J., 2024. Research on UAV high precision autonomous intelligent detection and evaluation system for large crane equipment metal structure, Proceedings of the 6th International Conference on Structural Health Monitoring and Integrity Management (ICSHMIM 2024), Zhengzhou, China, 8-10 November, NDT.net, 3057-2827.