Automatic pixel‐level multiple damage detection of concrete structure using fully convolutional network
Author:
Affiliation:
1. School of Civil Engineering, State Key Laboratory of Coastal and Offshore EngineeringDalian University of Technology Dalian China
2. Northeast Branch of China Construction Eighth Engineering Bureau Division Corp., LTD. Dalian China
Funder
National Key Research and Development Programs of China
Publisher
Wiley
Subject
Computational Theory and Mathematics,Computer Graphics and Computer-Aided Design,Computer Science Applications,Civil and Structural Engineering
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1111/mice.12433
Reference63 articles.
1. Analysis of Edge-Detection Techniques for Crack Identification in Bridges
2. Synchrosqueezed wavelet transform-fractality model for locating, detecting, and quantifying damage in smart highrise building structures
3. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
4. Defect Detection in Reinforced Concrete Using Random Neural Architectures
5. Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks
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