Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection

By Deegan Atha, Mohammad Reza Jahanshahi. 2018

320 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Industrial and Manufacturing Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/021fa253ebd9c5d78ee52b545a1ee915f0dafe27

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In Civil and Structural Engineering

In Industrial and Manufacturing Engineering

In Mechanical Engineering

Citations in the corpus

Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete (2018, methodology): “DCNNs have been used in vision-based structural health monitoring in recent years for crack detection 197 [42], road pavement cracks [55, 56], corrosion detection [57, 58], multi-damage detection [41, 59] structural 198 health monitoring [62].”
Generalized Out-of-Distribution Detection: A Survey (2021, unknown): “…of biometrics and art-works (Patel et al., 2016; Wen et al., 2015; Nixon et al., 2008; Polatkan et al., 2009), image forensics (Dolhansky et al., 2019; Jiang et al., 2021c; Yang et al., 2020c), industrial inspection (Bergmann et al., 2019; Chu & Kitani, 2020; Atha & Jahanshahi, 2018), etc..”
Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review (2020, methodology): “Wu and Jahanshahi [60] presented a study on the application of CNN for linear and nonlinear structural dynamic response estimation and identification.”
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring (2019, methodology): “Atha and Jahanshahi [117] evaluated different deep learning techniques for corrosion detection, and Chen and Jahanshahi [118] proposed the use of the naïve Bayes data fusion with a CNN for crack detection.”
Deep Learning for Anomaly Detection: A Survey (2019, background): “Several papers published utilizing deep learning models for detecting early industrial damage show great promise (Atha and Jahanshahi [2018], de Deijn [2018], Wang et al. [2018c]).”