Acknowledgement
본 연구는 국토교통과학기술진흥원의 기반시설 첨단관리(total care) 기술개발사업(RS-2022-00142566)의 지원으로 수행되었습니다. 이에 감사드립니다.
References
- Abdullah-Al-Mamun, M., Tyagi, V., Zhao, H. (2021), "A new full-reference image quality metric for motion blur profile characterization", IEEE Access, Vol. 9, pp. 156361-156371.
- Allaix, D.L., Vliet, A.B. (2023), "Existing standardization on monitoring, safety assessment and maintenance of bridges and tunnels", ce/papers, Vol. 6, No. 5, pp. 498-504.
- Artmann, U. (2013), "Image quality evaluation using moving targets", Proc. SPIE 8667, Multimedia Content and Mobile Devices, 86671D.
- Bae, H., Jang, K., An, Y.K. (2021), "Deep super resolution crack network (SrcNet) for improving computer vision-based automated crack detectability in in situ bridges", Structural Health Monitoring, Vol. 20, No. 4, pp. 1428-1442.
- Chan, F.H.Y., Lam, F.K., Zhu, H. (1998), "Adaptive thresholding by variational method", IEEE Transactions on Image Processing, Vol. 7, No. 3, pp. 468-473.
- Choi, S., Jun, H., Shin, S., Chung, W. (2021), "Evaluating accuracy of algorithms providing subsurface properties using full-reference image quality assessment", Geophysics and Geophysical Exploration, Vol. 24, No. 1, pp. 6-19.
- De, K., Masilamani, V. (2013), "Image sharpness measure for blurred images in frequency domain", Procedia Engineering, Vol. 64, pp. 149-158.
- Dinh, H., Wang, Q., Tu, F., Frymire, B., Mu, B. (2023), "Evaluation of motion blur image quality in video frame interpolation", Electronic Imaging, Vol. 35, pp. 262-1 - 262-5.
- Dost, S., Saud, F., Shabbir, M., Khan, M.G., Shahid, M., Lovstrom, B. (2022), "Reduced reference image and video quality assessments: review of methods", EURASIP Journal on Image and Video Processing, Vol. 2022, No. 1, pp. 1-31.
- Dugonik, B., Dugonik, A., Marovt, M., Golob, M. (2020), "Image quality assessment of digital image capturing devices for melanoma detection", Applied Sciences, Vol. 10, No. 8, 2876.
- Ferwerda, J.A. (2003), "Three varieties of realism in computer graphics", Proceedings of the SPIE: Human Vision and Electronic Imaging VIII, No. 5007, pp. 290-297.
- Galoogahi, H.K., Fagg, A., Huang, C., Ramanan, D., Lucey, S. (2017), "Need for speed: a benchmark for higher frame rate object tracking", Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, pp. 1125-1134.
- Huang, H.W., Li, Q.T., Zhang, D.M. (2018), "Deep learning based image recognition for crack and leakage defects of metro shield tunnel", Tunnelling and Underground Space Technology, Vol. 77, pp. 166-176.
- Huang, Z., Zhang, C.L., Fu, H.L., Ma, S.K., Fan, X.D. (2021), "Machine inspection equipment for tunnels: a review", Journal of Highway and Transportation Research and Development, Vol. 15, No. 2, pp. 40-53.
- Imatest Chart, https://www.imatest.com/product/large-iso-12233-digital-still-camera-resolution-chart-ink-jet-on-paper/ (March 10, 2024).
- Imatest MTF, https://www.imatest.com/docs/MTF_appearance/ (March 10, 2024).
- Imatest, https://www.imatest.com/ (March 10, 2024).
- ISO 12233:2023, https://www.iso.org/obp/ui/#iso:std:iso:12233:ed-4:v1:en (March 10, 2024).
- Jiang, H., Sun, D., Jampani, V., Yang, M.H., Learned-Miller, E., Kautz, J. (2018), "Super SloMo: high quality estimation of multiple intermediate frames for video interpolation", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, pp. 9000-9008.
- Kamble, V., Bhurchandi, K.M. (2015), "No-reference image quality assessment algorithms: a survey", Optik, Vol. 126, No. 11-12, pp. 1090-1097.
- Kamdi, S., Krishna, R.K. (2012), "Image segmentation and region growing algorithm", International Journal of Computer Technology and Electronics Engineering, Vol. 2, No. 1, pp. 103-107.
- Kawahara, S., Doi, H., Shirato, M., Kajifusa, N., Kutsukake, T. (2014), "Investigation of the tunnel ceiling collapse in the central expressway in Japan", Proceedings of the TRB 93rd Annual Meeting, Washington, D.C., USA, TRB Paper Manuscript #14-2559.
- Koren, N. (2006), "The Imatest program: comparing cameras with different amounts of sharpening", Proc. SPIE 6069, Digital Photography II, Vol. 60690, pp. 195-203.
- Krizhevsky, A., Sutskever, I., Hinton, G.E. (2012), "ImageNet classification with deep convolutional neural networks", Proceedings of the Advances in Neural Information Processing Systems, Nevada, USA, No. 25, pp. 1097-1105.
- Li, D., Xie, Q., Gong, X., Yu, Z., Xu, J., Sun, Y., Wang, J. (2021), "Automatic defect detection of metro tunnel surfaces using a vision-based inspection system", Advanced Engineering Informatics, Vol. 47, 101206.
- Liu, Y., Yeoh, J.K.W., Chua, D.K.H. (2020), "Deep learning-based enhancement of motion blurred UAV concrete crack images", Journal of Computing in Civil Engineering, Vol. 34, No. 5, 04020028.
- Masaoka, K. (2018), "Accuracy and precision of edge-based modulation transfer function measurement for sampled imaging systems", IEEE Access, Vol. 6, pp. 41079-41086.
- Montero, R., Victores, J.G., Martinez, S., Jardon, A., Balaguer, C. (2015), "Past, present and future of robotic tunnel inspection", Automation in Construction, Vol. 59, pp. 99-112.
- Moorthy, A.K., Bovik, A.C. (2011), "Blind image quality assessment: from natural scene statistics to perceptual quality", IEEE Transactions on Image Processing, Vol. 20, No. 12, pp. 3350-3364.
- Nah, S., Baik, S., Hong, S., Moon, G., Son, S., Timofte, R., Lee, K.M. (2019), "NTIRE 2019 challenge on video deblurring and super-resolution: dataset and study", Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA, pp. 1996-2005.
- Nah, S., Kim, T.H., Lee, K.M. (2017), "Deep multi-scale convolutional neural network for dynamic scene deblurring", Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp. 257-265.
- Ni, F., Zhang, J., Noori, M.N. (2020), "Deep learning for data anomaly detection and data compression of a long-span suspension bridge", Computer-Aided Civil and Infrastructure Engineering, Vol. 35, No. 7, pp. 685-700.
- NTSB (2007), Ceiling collapse in the interstate 90 connector tunnel, National Transportation Safety Board, NTSB/HAR-07/02, pp. 1-13.
- Pambrun, J.F., Noumeir, R. (2015), "Limitations of the SSIM quality metric in the context of diagnostic imaging", Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada, pp. 2960-2963.
- Paramanand, C., Rajagopalan, A.N. (2014), "Shape from sharp and motion-blurred image pair", International Journal of Computer Vision, Vol. 107, pp. 272-292.
- Rim, J., Lee, H., Won, J., Cho, S. (2020), "Real-world blur dataset for learning and benchmarking deblurring algorithms", Computer Vision - ECCV 2020, pp. 184-201.
- Salomon, D., Motta, G. (2010), Handbook of Data Compression, Springer, New York, pp. 443-730.
- Sankarasrinivasan, S., Balasubramanian, E., Karthik, K., Chandrasekar, U., Gupta, R. (2015), "Health monitoring of civil structures with integrated UAV and image processing system", Procedia Computer Science, No. 54, pp. 508-515.
- Shen, Z., Wang, W., Lu, X., Shen, J., Ling, H., Xu, T., Shao, L. (2019), "Human-aware motion deblurring", Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, pp. 5571-5580.
- Simonyan, K., Zisserman, A. (2015), "Very deep convolutional networks for large-scale image recognition", arXiv, Vol. 1409, 1556.
- Song, Q., Wu, Y., Xin, X., Yang, L., Yang, M., Chen, H., Liu, C., Hu, M., Chai, X., Li, J. (2019), "Real-time tunnel crack analysis system via deep learning", IEEE Access, Vol. 7, pp. 64186-64197.
- Sorel, M., Flusser, J. (2008), "Space-variant restoration of images degraded by camera motion blur", IEEE Transactions on Image Processing, Vol. 17, No. 2, pp. 105-116.
- Su, S., Delbracio, M., Wang, J., Sapiro, G., Heidrich, W., Wang, O. (2017), "Deep video deblurring for hand-held cameras", Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp. 237-246.
- Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A. (2015), "Going deeper with convolutions", Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp. 1-9.
- Telleen, J., Sullivan, A., Yee, J., Wang, O., Gunawardane, P., Collins, I., Davis, J. (2007), "Synthetic shutter speed imaging", Computer Graphics Forum, Vol. 26, No. 3, pp. 591-598.
- Ukai, M. (2007), "Advanced inspection system of tunnel wall deformation using image processing", Quarterly Report of RTRI, No. 48, No. 2, pp. 94-98.
- Wang, H., Wang, Q., Zhai, J., Yuan, D., Zhang, W., Xie, X., Zhou, B., Cai, J., Lei, Y., (2022), "Design of fast acquisition system and analysis of geometric feature for highway tunnel lining cracks based on machine vision", Applied Sciences, Vol. 12, No. 5, 2516.
- Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P. (2004), "Image quality assessment: from error visibility to structural similarity", IEEE Transactions on Image Processing, Vol. 13, No. 4, pp. 600-612.
- Xiang, C., Wang, W., Deng, L., Shi, P., Kong, X. (2022), "Crack detection algorithm for concrete structures based on super-resolution reconstruction and segmentation network", Automation in Construction, Vol. 140, 104346.
- Xue, Y., Li, Y. (2018), "A fast detection method via region-based fully convolutional neural networks for shield tunnel lining defects", Computer-Aided Civil and Infrastructure Engineering, Vol. 33, No. 8, pp. 638-654.
- Yang, W., Zhang, X., Tian, Y., Wang, W., Xue, J.H., Liao, Q. (2019), "Deep learning for single image super-resolution: a brief review", IEEE Transactions on Multimedia, Vol. 21, No. 12, pp. 3106-3121.
- Yasuda, T., Yamamoto, H., Shigeta, Y. (2016), "Tunnel inspection system by using high-speed mobile 3D survey vehicle: MIMM-R", Journal of Robotics Society of Japan, Vol. 34, No. 9, pp. 589-590.
- Ye, X.W., Jin, T., Yun, C.B. (2019), "A review on deep learning-based structural health monitoring of civil infrastructures", Smart Structures and Systems, Vol. 24, No. 5, pp. 567-585.
- Yu, Y., Samali, B., Rashidi, M., Mohammadi, M., Nguyen, T.N., Zhang, G. (2022), "Vision-based concrete crack detection using a hybrid framework considering noise effect", Journal of Building Engineering, Vol. 61, 105246.