Deep convolutional neural networks for detection of rail surface defects

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TDD-net: a tiny defect detection network for printed circuit boards (2019, background): “[19] presented a deep-learning approach for automatic detection of rail surface defects.”
Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network (2020, background): “Similarly, the authors in [28] proposed segmentation architecture for surface defect detection based on deep learning.”
Remaining Useful Lifetime Prediction via Deep Domain Adaptation (2019, background): “Neural Networks have been receiving much attention given their ability to approximate high dimensional non-linear functions directly from raw data [9].”
Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization (2020, unknown): “Some of the recent applications of DL approaches on material science include material classification [35, 36], defect classification [37–39], microstructure identification [40, 41], microstructure reconstruction [42, 43], composite strength prediction [44], etc.”
Segmentation-based deep-learning approach for surface-defect detection (2019, unknown): “When applied to the problem of surface-quality con-trol (Chen and Ho 2016; Faghih-Roohi et al 2016; Weimer et al 2013; Kuo et al 2014), deep-learning methods can achieve excellent results and can be adapted to different products.”