Vision-based defects detection for bridges using transfer learning and convolutional neural networks-Reference-Cited by-同舟云学术

Vision-based defects detection for bridges using transfer learning and convolutional neural networks

Published:2019-11-27 Issue:7 Volume:16 Page:1037-1049
ISSN:1573-2479
Container-title:Structure and Infrastructure Engineering
language:en
Short-container-title:Structure and Infrastructure Engineering

Author:

Zhu Jinsong¹,Zhang Chi¹,Qi Haidong²,Lu Ziyue¹

Affiliation:

1. Key Laboratory of Coast Civil Structure Safety (Ministry of Education), School of Civil Engineering, Tianjin Univ, Tianjin, People’s Republic of China;

2. Municipal construction administration department of Huailai, Huailai, Hebei, People’s Republic of China

Publisher

Informa UK Limited

Subject

Mechanical Engineering,Ocean Engineering,Geotechnical Engineering and Engineering Geology,Safety, Risk, Reliability and Quality,Building and Construction,Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/15732479.2019.1680709

Reference29 articles.

1. Output-only computer vision based damage detection using phase-based optical flow and unscented Kalman filters

2. Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks

3. Long-term bridge health monitoring and performance assessment based on a Bayesian approach

4. Identification of spatio-temporal distribution of vehicle loads on long-span bridges using computer vision technology

5. Learning rates and their impacts on the optimal capacities and production costs of biorefineries

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