Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection

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A Comprehensive Survey on Transfer Learning (2019, background): “Transfer learning can also be utilized for anomalous activity detection [188], [189], traffic sign recognition [190], and so on”
A review of computer vision–based structural health monitoring at local and global levels (2020, methodology): “Gopalakrishnan et al.56 truncated the VGG-16 network which was pretrained on ImageNet dataset and retrained the modified VGG-16 with dataset from FHWA Long-Term Pavement Performance (LTPP) database by using transfer learning.”
Encoder–decoder network for pixel‐level road crack detection in black‐box images (2019, background): “Gopalakrishnan, Khaitan, Choudhary, and Agrawal (2017) employed a CNN model trained via transfer learning to automatically detect cracks in images of road pavement consisting of hot-mix asphalt and Portland cement concrete.”
CrackU‐net: A novel deep convolutional neural network for pixelwise pavement crack detection (2020, methodology): “Gopalakrishnan et al. 21 trained a deep convolutional neural network model for automatic pavement crack detection, which is transferred from the pretrained models of ImageNet VGG16.”
Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets (2018, methodology): “[58] applied transfer learning technique to detect crack in pavement.”