최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기韓國ITS學會 論文誌 = The journal of the Korea Institute of Intelligent Transportation Systems, v.20 no.4, 2021년, pp.95 - 105
심승보 (한국건설기술연구원 인프라안전연구본부)
Road surface damage detection is essential for a comfortable driving environment and the prevention of safety accidents. Road management institutes are using automated technology-based inspection equipment and systems. As one of these automation technologies, a sensor to detect road surface damage p...
Bang S., Park S., Kim H. and Kim H.(2018), "A deep residual network with transfer learning for pixel-level road crack detection," In Proc. International Symposium on Automation and Robotics in Construction, Berlin, Germany, vol. 35, pp.1-4.
Bang S., Park S., Kim H. and Kim H.(2019), "Encoder-decoder network for pixel level road crack detection in black-box images," Computer-Aided Civil and Infrastructure Engineering, vol. 34, no. 8, pp.713-727.
Fan R.(2018), Real-time computer stereo vision for automotive applications, Doctoral Dissertation, University of Bristol.
Fan Z., Li C., Chen Y., Wei J., Loprencipe G., Chen X. and Di Mascio P.(2020), "Automatic crack detection on road pavements using encoder-decoder architecture," Materials, vol. 13, p.2960.
Feng H., Xu G. S. and Guo Y.(2018), "Multi-scale classification network for road crack detection," IET Intelligent Transport Systems, vol. 13, no. 2, pp.398-405.
Goodfellow I. J., Pouget-Abadie J., Mirza M., Xu B., Warde-Farley D., Ozair S., Courville A. and Bengio Y.(2014), Generative adversarial networks [Online], arXiv:1406.2661. Available at https://arxiv.org/abs/1406.2661
Guan H., Li J., Yu Y., Chapman M. and Wang C.(2014), "Automated road information extraction from mobile laser scanning data," IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 1, pp.194-205.
He K., Zhang X., Ren S. and Sun J.(2016), "Deep residual learning for image recognition," In Proc. IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp.770-778.
Huang G., Liu Z., Van Der Maaten L. and Weinberger K. Q.(2017), "Densely connected convolutional networks," In Proc. IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp.4700-4708.
Hung W. C., Tsai Y. H., Liou Y. T., Lin Y. Y. and Yang M. H.(2018), Adversarial learning for semi-supervised semantic segmentation [Online], arXiv:1802.07934. Available at https://arxiv.org/abs/1802.07934
Jenkins M. D., Carr T. A., Iglesias M. I., Buggy T. and Morison G.(2018), "A deep convolutional neural network for semantic pixel-wise segmentation of road and pavement surface cracks," In Proc. 26th European Signal Processing Conference(EUSIPCO), Rome, Italy pp.2120-2124.
Kingma D. P. and Ba J.(2014), Adam: A method for stochastic optimization [Online], arXiv:1412.6980. Available at https://arxiv.org/abs/1412.6980
Laurent J., Hebert J. F., Lefebvre D. and Savard Y.(2012), "Using 3D laser profiling sensors for the automated measurement of road surface conditions," In Proc. 7th RILEM International Conference on Cracking in Pavements, Delft, Netherlands, pp.157-167.
Li G., Wan J., He S., Liu Q. and Ma B.(2020), "Semi-supervised semantic segmentation using adversarial learning for pavement crack detection," IEEE Access, vol. 8, pp.51446-51459.
Madli R., Hebbar S., Pattar P. and Golla V.(2015), "Automatic detection and notification of potholes and humps on roads to aid drivers," IEEE Sensors Journal, vol. 15, no. 8, pp.4313-4318.
Mei Q., Gul M. and Azim M. R.(2020), "Densely connected deep neural network considering connectivity of pixels for automatic crack detection," Automation in Construction, vol. 110, p.103018.
Ouali Y., Hudelot C. and Tami M.(2020), An overview of deep semi-supervised learning [Online], arXiv:2006.05278. Available at https://arxiv.org/abs/2006.05278
Romera E., Alvarez J. M., Bergasa L. M. and Arroyo R.(2017), "Erfnet: Efficient residual factorized convnet for real-time semantic segmentation," IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 1, pp.263-272.
Shi Y., Cui L., Qi Z., Meng F. and Chen Z.(2016), "Automatic road crack detection using random structured forests," IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 12, pp.3434-3445.
Shim S., Kim J., Cho G. C. and Lee S. W.(2020), "Multiscale and adversarial learning-based semi-supervised semantic segmentation approach for crack detection in concrete structures," IEEE Access, vol. 8, pp.170939-170950.
Singla A., Bertino E. and Verma D.(2019), "Overcoming the lack of labeled data: Training intrusion detection models using transfer learning," In Proc. IEEE International Conference on Smart Computing(SMARTCOMP), Washington, DC, USA, pp.69-74.
Zhang A., Wang K. C. P., Fei Y., Liu Y., Chen C., Yang G., Li J. Q., Yang E. and Qiu S.(2019), "Automated pixel-level pavement crack detection on 3D asphalt surfaces with a recurrent neural network," Computer-Aided Civil and Infrastructure Engineering, vol. 34, no. 3, pp.213-229.
Zhang Y., Wang S., Chen B., Cao J. and Huang Z.(2021), "TrafficGAN: Network-scale deep traffic prediction with generative adversarial nets," IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 1, pp.219-230.
Zou Q., Zhang Z., Li Q., Qi X., Wang Q. and Wang S.(2018), "Deepcrack: Learning hierarchical convolutional features for crack detection," IEEE Transactions on Image Processing, vol. 28, no. 3, pp.1498-1512.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
출판사/학술단체 등이 한시적으로 특별한 프로모션 또는 일정기간 경과 후 접근을 허용하여, 출판사/학술단체 등의 사이트에서 이용 가능한 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.