Mobile QR Code QR CODE

Journal of the Korea Concrete Institute

J Korea Inst. Struct. Maint. Insp.
  • Indexed by
  • Korea Citation Index (KCI)

References

1 
Ministry of Land, Infrastructure and Transport., (2018), Detailed guidelines for safety and maintenance of facilities (performance evaluation), in KoreanGoogle Search
2 
Ministry of Oceans and Fisheries., (2018), Harbor and fishing port fender maintenance manual, in KoreanGoogle Search
3 
Ministry of Oceans and Fisheries., (2015), Detailed guidelines for safety inspection of port facilities, in KoreanGoogle Search
4 
Ministry of Oceans and Fisheries., (2020), Port facility maintenance and mid- to long-term road map establishment project, in KoreanGoogle Search
5 
Sakakibara, S., Kubo, M. (2007), Ship berthing and mooring monitoring system by pneumatic-type fenders, Ocean engineering, Elsevier, 34(8-9), 1174-1181.DOI
6 
Yamada, S., Sakakibara, S., Miyamoto, M., Nakatani, K. (2012), Final acceptance test on advanced ship maneuvering and mooring support system at ship-to-ship transfer operations by fender monitoring system of pneumatic fenders, The twenty-second International offshore and polar engineering conference Rhodes, 901-908.URL
7 
Girshick, R., Donahue, J., Darrell, T., Malik, J. (2014), Rich feature hierarchies for accurate object detection and semantic segmentation, Proceedings of the IEEE conference on computer vision and pattern recognition, Columbus, 580-587.URL
8 
Girshick, R. (2015), Fast r-cnn, Proceedings of the IEEE international conference on computer vision, Santiago, 1440-1448.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, Montreal, 91-99.URL
10 
Redmon, J., Divvala, S., Girshick, R., Farhadi, A. (2016), You only look once: Unified, real-time object detection, Proceedings of the IEEE conference on computer vision and pattern recognition Las Vegas, 779-788.URL
11 
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., Berg, A.C. (2016), SSD: Single shot multibox detector, European conference on computer vision, Amsterdam, 21-37.DOI
12 
He, K., Gkioxari, G., Dollár, P., Girshick, R. (2017), Mask r-cnn, Proceedings of the IEEE international conference on computer vision, Venice, 2961-2969.URL
13 
Long, J., Shelhamer, E., Darrell, T. (2015), Fully convolutional networks for semantic segmentation, Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, 3431-3440.URL
14 
Ronneberger, O., Fischer, P., Brox, T. (2015), U-Net: Convolutional Networks for Biomedical Image Segmentation, Proceedings of the International conference on medical image computing and computer-assisted intervention ,Munich, 234-241.DOI
15 
Dung, C. V., Anh, L. D. (2019), Autonomous concrete crack detection using deep fully convolutional neural network, Automation in Construction, 99, 52-58.DOI
16 
Islam, M.M.M., Kim, J.-M. (2019), Vision-based autonomous crack detection of concrete structures using a fully convolutional encoder–decoder network, Sensors, 19(4251)DOI
17 
Dong, C., Li, L., Yan, J., Zhang, Z., Pan, H., Catbas, F.N. (2021), Pixel-level fatigue crack segmentation in large-scale images of steel structures using an encoder–decoder network, Sensors, 21(4135)DOI
18 
Liu, S., Huang, D. (2018), Receptive field block net for accurate and fast object detection, Proceedings of the European Conference on Computer Vision (ECCV), Munich, 385-400.URL
19 
Wang, H., Su, D., Liu, C., Jin, L., Sun, X., Peng, X. (2019), Deformable non-local network for video super-resolution, IEEE Access, 7, 177734-177744.DOI
20 
Simonyan, K., Zisserman, A. (2015), Very deep convolutional networks for large-scale image recognition, Proceedings of the 3rd International conference on leaning representationsDOI