A Fast Detection Method via Region‐Based Fully Convolutional Neural Networks for Shield Tunnel Lining Defects

By Ya-dong Xue, Yicheng Li. Comput. Aided Civ. Infrastructure Eng., 2018

327 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/365b31515028f2c7444d5b5afd865d242b229bd2

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In Civil and Structural Engineering

In Ocean Engineering

Citations in the corpus

SDDNet: Real-Time Crack Segmentation (2020, methodology): “Therefore, ﬂexible localization methods were required, and faster R-CNN [13] was adopted in the SHM research on localizing damage [14], [15], [16].”
Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges (2020, background): “It mainly includes convolutional neural network (CNN) [75], autoencoder neural network [74,76], deep residual neural network [77], full convolution neural network [78], and recurrent neural network [79,80].”
Image‐based post‐disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization (2018, unknown): “…the detection and localization of multiple types of damage (Cha, Choi, Suh, Mahmoudkhani, & Büyüköztürk, 2018; Suh & Cha, 2018), shield tunnel lining defects detection (Xue & Li, 2018), and concrete defect detection and geolocalization with a uniﬁed methodology (Li, Yuan, Zhang, & Yuan, 2018).”
A systematic review of convolutional neural network-based structural condition assessment techniques (2021, methodology): “483 The capabilities of region-based FCN were explored by Xue and Li (2018) for shield tunnel lining 484 defects.”
Automatic pixel‐level multiple damage detection of concrete structure using fully convolutional network (2019, methodology): “However, the CNN method has to adopt a sliding window technique to localize the detected damage (Cha et al., 2017a; Xue & Li, 2018).”