# A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
## 2 Automatic Feature Extraction
### Semantic Segmentation
#### 2.3.2 Pure Semantic Segmentation
The difference between these studies is that in [91], more than one pixel in the centre define whether the patch is positive or not resulting in thicker crack masks after detection. On the other hand, in [92], only the centre pixel is considered as the criterion. The same idea of crack structure prediction in [92] was employed to perform crack detection with high accuracy by [PERSON] et al. [93]. It is worth noting that they removed the pooling layers from the main structure to avoid loosing spatial information and crack quantification also was considered [93]. CrackNet II [94] and CrackNet-V [95] as improvements to the CrackNet [96], in terms of both learning capability and the required processing time, were proposed. It is worth mentioning that the CrackNet framework is based on both handcrafted features and deep learning. However, CrackNet II and CrackNet-V are deep learning-based approaches utilised for SS of 3D crack images. Both approaches do not include any pooling layers and the image width and height are invariant through the consecutive convolution layers resulting in a supervised classification at the pixel-level. The difference between CrackNet II and CrackNet-V approaches is that CrackNet-V, in addition to a set of consecutive convolution layers, consists of a pre-processing module based on the median filter and Z-score normalization. It must be added that the same research group also proposed Crack Net-R [97] as another improved version of CrackNet architecture, based on the application of recurrent neural network (RNN) for the task of crack segmentation. In order to perform SS via the application of an encoder-decoder structure, a backbone architecture is used to extract deep features which is formed by a sequence of convolution, pooling, and activation layers. Since the width and height of the input image decrease once it passes through the backbone architecture, a decoder module, which is formed by a sequence of deconvolution (aka transposed convolution or fractionally strided convolution) layers, is employed to restore the size of the features to the original image size so that the classification can be done at the pixel-level [98]. The encoder-decoder structure has been abundantly applied to perform crack segmentation. Among various well-known architectures that have been proposed for performing SS in the computer vision area, U-Net [99], SegNet [87], and FC-DenseNet [100] have been the most considered in the crack detection area. It must be added that to achieve higher accuracy in the SS setting, it is important to let the contextual information flow in the architecture [101]. To achieve this, various approaches have been considered in the crack detection area, as follows. **U-Net** is an architecture first proposed in the area of medical image analysis for biomedical image segmentation [99]. To the best of the authors' knowledge, [PERSON] et al. [102] performed the first application of U-Net architecture in the crack detection area and showed its superiority for the task of crack segmentation over approaches based on handcrafted features combined with classical machine learning algorithms. It must be added that in the crack detection area, there is a challenge of class imbalance because of having access to more background pixels compared to crack pixels. To solve this challenge, different techniques and approaches have been proposed that, in the case of some studies, could be considered as one of the contributions of the study to the field. In [103], U-Net architecture was equipped with a new loss function based on distance transform to deal with the challenge of class imbalance in the crack detection area. To further improve the performance of U-Net for crack segmentation, [PERSON] et al. [104] combined the U-Net architecture with residual connections and an attention gating mechanism. An ablation study related to the application of U-Net architecture for crack segmentation was carried out in [105].