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Yasutaka Narazaki
Yasutaka Narazaki
Other names楢崎泰隆
Assistant Professor, ZJU-UIUC Institute, Zhejiang University
Verified email at intl.zju.edu.cn - Homepage
Title
Cited by
Cited by
Year
Advances in computer vision-based civil infrastructure inspection and monitoring
BF Spencer Jr, V Hoskere, Y Narazaki
Engineering 5 (2), 199-222, 2019
8082019
Vision-based structural inspection using multiscale deep convolutional neural networks
V Hoskere, Y Narazaki, T Hoang, BF Spencer Jr
arXiv preprint arXiv:1805.01055, 2018
1192018
Vision‐based automated bridge component recognition with high‐level scene consistency
Y Narazaki, V Hoskere, TA Hoang, Y Fujino, A Sakurai, BF Spencer Jr
Computer‐Aided Civil and Infrastructure Engineering 35 (5), 465-482, 2020
922020
MaDnet: multi-task semantic segmentation of multiple types of structural materials and damage in images of civil infrastructure
V Hoskere, Y Narazaki, TA Hoang, BF Spencer Jr
Journal of Civil Structural Health Monitoring 10 (5), 757-773, 2020
892020
Towards automated post-earthquake inspections with deep learning-based condition-aware models
V Hoskere, Y Narazaki, TA Hoang, BF Spencer Jr
arXiv preprint arXiv:1809.09195, 2018
692018
Sensor fault management techniques for wireless smart sensor networks in structural health monitoring
Y Fu, C Peng, F Gomez, Y Narazaki, BF Spencer Jr
Structural Control and Health Monitoring 26 (7), e2362, 2019
542019
Synthetic environments for vision-based structural condition assessment of Japanese high-speed railway viaducts
Y Narazaki, V Hoskere, K Yoshida, BF Spencer, Y Fujino
Mechanical Systems and Signal Processing 160, 107850, 2021
512021
Synthetic data augmentation for pixel-wise steel fatigue crack identification using fully convolutional networks
G Zhai, Y Narazaki, S Wang, SAV Shajihan, BF Spencer Jr
Smart Struct Syst 29 (1), 237-250, 2022
432022
Efficient development of vision-based dense three-dimensional displacement measurement algorithms using physics-based graphics models
Y Narazaki, F Gomez, V Hoskere, MD Smith, BF Spencer
Structural Health Monitoring 20 (4), 1841-1863, 2021
382021
Physics-based graphics models in 3D synthetic environments as autonomous vision-based inspection testbeds
V Hoskere, Y Narazaki, BF Spencer Jr
Sensors 22 (2), 532, 2022
342022
Automated bridge component recognition using video data
Y Narazaki, V Hoskere, TA Hoang, BF Spencer Jr
arXiv preprint arXiv:1806.06820, 2018
322018
Vision-based automated bridge component recognition integrated with high-level scene understanding
Y Narazaki, V Hoskere, TA Hoang, BF Spencer
arXiv preprint arXiv:1805.06041, 2018
312018
Vision-based navigation planning for autonomous post-earthquake inspection of reinforced concrete railway viaducts using unmanned aerial vehicles
Y Narazaki, V Hoskere, G Chowdhary, BF Spencer Jr
Automation in Construction 137, 104214, 2022
292022
Automated vision-based bridge component extraction using multiscale convolutional neural networks
Y Narazaki, V Hoskere, TA Hoang, BF Spencer Jr
arXiv preprint arXiv:1805.06042, 2018
262018
Advances in computer vision-based civil infrastructure inspection and monitoring. Engineering, 5 (2), 199–222
BF Spencer, V Hoskere, Y Narazaki
252019
Learning to detect important visual changes for structural inspections using physicsbased graphics models
V Hoskere, Y Narazaki, BF Spencer
9th International Conference on Structural Health Monitoring of Intelligent …, 2019
252019
Vision-based dense displacement and strain estimation of miter gates with the performance evaluation using physics-based graphics models
Y Narazaki, V Hoskere, BA Eick, MD Smith, BF Spencer
Smart Structures and Systems, An International Journal 24 (6), 709-721, 2019
252019
Deep learning-based damage detection of miter gates using synthetic imagery from computer graphics
V Hoskere, Y Narazaki, BF Spencer, MD Smith
12th international workshop on structural health monitoring: Enabling …, 2019
252019
Recent progress of triboelectric nanogenerators as self-powered sensors in transportation engineering
AM Nazar, Y Narazaki, A Rayegani, FR Sardo
Measurement 203, 112010, 2022
202022
Percussion-based quasi real-time void detection for concrete-filled steel tubular structures using dense learned features
D Chen, Z Shen, L Huo, Y Narazaki
Engineering Structures 274, 115197, 2023
182023
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