Vision‐Based Automated Crack Detection for Bridge Inspection

By Chul Min Yeum, Shirley J. Dyke. Comput. Aided Civ. Infrastructure Eng., 2015

482 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/51fc4d5e64fad5d70ffde8e0870c9b85dd47c8d7

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In Civil and Structural Engineering

In Mechanical Engineering

Citations in the corpus

Deep Transfer Learning for Image‐Based Structural Damage Recognition (2018, background): “However, several recent non-DL studies are addressing issues related to relatively tedious manual efforts (Feng and Feng, 2017; Yoon et al., 2016; Yeum and Dyke, 2015; Torok et al., 2013).”
Applications of artificial intelligence for disaster management (2020, unknown): “2019), bridges (Yeum and Dyke 2015), tunnels (Victores et al.”
Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types (2018, background): “…damage, such as concrete cracks (Abdel-Qader et al., 2003; Nishikawa et al., 2012), concrete spalling (German et al., 2012), steel cracks (Yeum and Dyke, 2015), loosened bolts (Park et al., 2015; Cha et al., 2016), road pavement cracks (Ying and Salari, 2010; Cord and Chambon, 2012;…”
Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks (2017, background): “Yeum and Dyke (2015) proposed a study for detecting steel cracks using IPTs combined with a sliding window technique; this article shows the potential of IPTs very well.”
Image‐based post‐disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization (2018, background): “Much of the research in defect detection and assessment largely focused on cracks (e.g., Abdel-Qader, Abudayyeh, & Kelly, 2003; Feng & Feng, 2017; Torok, Golparvar-Fard, & Kochersberger, 2013; Yamaguchi & Hashimoto 2010; Yeum & Dyke, 2015; Yoon, Comput Aided Civ Inf.”
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring (2019, background): “Yeum and Dyke [89] manually created defects on a steel beam to give the appearance of fatigue cracks (Fig.”
Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network (2018, methodology): “To increase the robustness of illumination and shading variance, general global transforms and local edge detection were introduced to crack contour detection, such as fast Haar transform (FHT), fast Fourier transform (FFT), Sobel and Canny edge detectors (Sinha and Fieguth, 2006; Yu et al., 2007; S”