NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion

By Fu-Chen Chen, Mohammad Reza Jahanshahi. IEEE transactions on industrial electronics (1982. Print), 2018

805 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/414170e0f6959ec452c6da866a61a2a557682f56

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

In Industrial and Manufacturing Engineering

In Mechanical Engineering

Citations in the corpus

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.”
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].”
A systematic review of convolutional neural network-based structural condition assessment techniques (2021, methodology): “Chen and Jahanshahi 638 (2018) proposed a CNN method with a naïve Bayes data fusion scheme to detect tiny cracks on 639 metallic surfaces from video data for nuclear inspection applications.”
Encoder–decoder network for pixel‐level road crack detection in black‐box images (2019, background): “In particular, CNN—a type of deep neural network— has attracted attention because it can extract the meaningful features from the input image without human-involved feature engineering (Chen & Jahanshahi, 2018; Krizhevsky, Sutskever, & Hinton, 2012; Simonyan & Zisserman, 2014; Szegedy et al., 2015).”
Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review (2020, background): “Deep learning and CNN scholars did not limit their scope in the field of image recognition, and attempted diverse applications to detect crack damage in real time for instance using unmanned aerial vehicles or drones, as illustrated in [23, 67–69 , 79].”
Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture (2019, methodology): “An NB-CNN method is proposed based on CNN and a Naive Bayes data fusion scheme to aggregate the information extracted from each video frame and enhance the overall performance of the system [22].”
Tackling Climate Change with Machine Learning (2019, background): “[112] Fu-Chen Chen and Mohammad R Jahanshahi.”
PGA-Net: Pyramid Feature Fusion and Global Context Attention Network for Automated Surface Defect Detection (2020, methodology): “[34] proposed an approach based on CNN, which analyzed individual video frames for crack detection through CNN and Naïve Bayes datafusionscheme.”