최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기大韓建築學會論文集. Journal of the architectural institute of korea. 構造系, v.35 no.11 = no.373, 2019년, pp.163 - 170
정서영 (광운대학교 대학원 건축공학과) , 이슬기 (광운대학교 건축공학과) , 박찬일 (광운대학교 대학원 전자통신공학과) , 조수영 (광운대학교 대학원 전자통신공학과) , 유정호 (광운대학교 건축공학과)
Most of the current crack investigation work consists of visual inspection using simple measuring equipment such as crack scale. These methods involve the subjection of the inspector, which may lead to differences in the inspection results prepared by the inspector, and may lead to a large number of...
Byun, T., Kim, J., & Kim, H. (2006). The Recognition of Crack Detection Using Difference Image Analysis Method based on Morphology, Journal of the Korea Institute of Information and Communication Engineering, 10(1), 197-205.
Cha, Y., Choi, W., & Oral, B. (2017). Deep LearningBased Crack Damage Detection Using Convolutional Neural Networks, Computer-Aided Civil and Infrastructure Engineering, 32(5), 361-378.
Chen, L., Jan, H., & Huang, C. (2001). Mensuration of Concrete Cracks Using Digitized Close-Range Photographs, The 22nd Asian conference of Remote Sensing, 5-9.
Cho, S., Kim, B., & Lee, Y. (2018). Image-Based Concrete Crack and Spalling Detection using Deep Learning, The Magazine of the Korean Sosiety of Civil Engineers, 66(8), 92-97.
Donglai, wei., Bolei, Whou., Antonio, Torralba., and William, T.Freema. (2015). "mNeuron: A Maltlab Plugin to Visualize neurons From Deep Models"
Joseph, R., Santosh, Divvala., Ross, G., & Ali, F. (2016). You Only Look Once: Unified, Real-Time Object Detection, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779-788.
Joseph, R., & Ali, F. (2017). YOLO9000: Better, Faster, Stronger, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1-9.
Kim, A., Kim, D., Byun, Y., & Lee, S. (2018). Crack Detection of Concrete Structure Using Deep Learning and Image Processing Method in Geotechnical Engineering, Journal of the Korean Geotechnical Society, 34(12), 145-154.
Kim, K., Cho, J., & Ahn, S. (2005). A Thechnique for Image Processing of Concrete Surface Cracks, Journal of the Korea Institute of Information and Communication Engineering, 9(7), 1575-1581.
Kim, J., & Cho, Y. (2002). Development of Crack Detection Program on Asphalt Pavement, Journal of the Korean Society Of Civil Engineers, 22(4D), 639-647.
Kim, J., Jung, Y., & Rhim, H. (2017). Study on Structure Visual Inspection Technology using Drones and Image Analysis Techniques, Journal of the Korean Institute of Building Construction, 17(6), 545-557.
K, Lenc., & A, Vedaldi. (2015). R-cnn minus r. arXiv preprintarXiv:1506.06981.
Kim, Y. (2016). Development of Crack Recognition System for Concrete Structure Using Image Processing Method, The Journal of Korean Institute of Information Technology, 14(10), 163-168.
Ko, K., & Sim, K. (2017). Trends in Object Recognition and Detection Using Deep Learning, Journal of Institute of Control, Robotics and Systems, 23(3), 17-24.
Lee, J., & Kim, K. (2007). Extraction and Analysis of Crack on Concrete Surfaces Using Improved Image Processing Techniques, Proceeding of the Korea Intelligent Information Systems Society Conference, 365-372.
R, B, Girshick. (2015). Fast R-CNN. CoRR, abs/1504.08083, 2015.
Son, B., & Lee, K. (2017). Crack Recognition of Sewer with Low Resolution using Convolutional Neural Network(CNN) Method, Journal of the Korean Society for Advanced Composite Structures, 8(4) 58-65.
Syed, I, H., Dang, L, M., Im, S., Min, K., Nam, J., & Moon, H. (2018). Damage Detection and Classification System for Sewer Inspection using Convolutional Neural Networks based on Deep Learning, Journal of the Korea Institute of Information and Communication Engineering, 22(3), 451-457.
S, Ren., K, He., R, Girshick., & J, Sun. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. arXiv preprint arXiv:1506.01497.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, & S. E. eed. (2015). SSD: single shot multibox detector. CoRR, abs/1512.02325.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.