# A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
## 2 Automatic Feature Extraction
### Semantic Segmentation
#### 2.3.2 Pure Semantic Segmentation  
Pure crack segmentation is performed with no crack patch or crack candidate region identification beforehand. Therefore, no IC or OR approach is involved in this procedure. In the literature, several approaches have been considered for performing pure SS with an application for crack detection. Pure SS can be done by the typical architecture utilised for IC if the fully connected layers are replaced with convolutional layers resulting in an FCN [89; 90] (aka an encoder-decoder structure). This opens a window to different FCN structures that can be used for SS in the area of computer vision. In the area of crack detection, mostly, the pure crack segmentation approaches are performed with the application of encoder-decoder structures. However, several studies have employed other techniques to perform crack segmentation at the pixel-level. Firstly, these approaches and then, the encoder-decoder approaches will now be reviewed. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref** & **Novelty/Novelies** & **Method** & **Core Architecture** \\ \hline
[78] & 1. Application of transfer learning to identify crack and sealed crack patches. 2. Applying fast block-wise segmentation based on linear regression to segment the identified patches. 3. Application of tensor voting curve detection to extract the detected crack curves. & IC + IP & - \\ \hline
[88] & 1. Application of pre-trained AlexNet on ImageNet data set to classify road images into the crack, sealed crack, and background patches. 2. Application of an FCN combined with dilated convolution to segment the detected crack patches. & IC + FCN & AlexNet \\ \hline
[85] & Proposing a crack delineation network including a generic pre-trained CNN model (GoogLeNet) combined with a feature pyramid network to achieve feature-map fusion. & IC + FCN & GoogLeNet \\ \hline
[77] & 1. Proposing a dual-scale CNN to perform crack patch classification and segmentation. 2. Application of Zernike moment operator for quantitative crack width estimation. & IC + IP & GoogLeNet \\ \hline
[80] & Application of mask R-CNN for bughole segmentation in concrete surface images and quantification of the segmented bugholes. & OR + FCN & Mask R-CNN \\ \hline
[84] & Application of mask R-CNN to detect cracks in pavement image data sets & OR + FCN & Mask R-CNN \\ \hline
[83] & Application of faster R-CNN combined with a Bayesian probability algorithm to suppress false detection and a set of IPTs to perform crack segmentation. & OR + IP & Faster R-CNN \\ \hline
[81] & 1. Application of mask R-CNN to output crack masks. 2. Applying IPTs to quantify the detected masks. & OR + FCN & Mask R-CNN \\ \hline
[86] & Proposing a new hybrid crack segmentation approach based on the global non-overlapping sliding windows and Sobel-edge detector to identify crack patches combined with a deep encoder–decoder architecture (SegNet) to perform crack segmentation. & IC + FCN & SegNet \\ \hline
[75] & Performing a comparative study between two crack segmentation frameworks of (i) Faster R-CNN combined with structured Random Forest edge detection and (ii) Mask R-CNN. & OR + IP and OR + FCN & Faster R-CNN, Mask R-CNN \\ \hline
[76] & 1. Performing crack segmentation using Faster R-CNN combined with a modified tabularity flow field. 2. Performing crack quantification using a modified distance transform method. & OR + IP & Faster R-CNN \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of deep crack segmentation approaches based on the hybrid SS setting. Crack segmentation at the pixel-level was done with an application of the IC architecture in [91, 92]. In both of these studies, once the patches are extracted from the pavement images, crack patches (i.e., positive patches) are defined based on a criterion of whether the centre pixels are crack ones or not. After training and classification of the positive patches, the crack mask at the pixel-level can be achieved by stacking the positive pixels together.