![]() Thus more detailed review and categorization of these studies at deeper levels and from different points of view is necessary, to help interested scholars to more fully understand and compare the proposed approaches. ![]() It is obvious that the number of published studies based on deep learning architectures in the crack detection area is growing fast. As can be seen, the application of SS has become the most popular setting in the crack detection area in the past years. To gain insight into the application trend of each settings in the crack detection area, Figure 1 is provided. Mainly, the difference between each setting is the level at which the crack detection is performed (e.g., image-, image patch-, or pixel-level). Numerous deep learning-based crack detection studies have been proposed which are categorised into studies based on (i) image classification (IC), (ii) object recognition (OR), and (iii) semantic segmentation (SS) settings. Deep learning is a branch of machine learning in which NNs with deep layers are used to gradually perform feature extraction and the architecture itself decides which features are relevant.
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