# A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
## 2 Automatic Feature Extraction
### Semantic Segmentation
#### 2.3.1 Hybrid Semantic Segmentation  
We use the word \"hybrid\" in this subcategory because the procedure of crack segmentation is involved with the application of the two aforementioned settings (i.e., either IC or OR). In other words, as the first step, either an IC setting (or an OR setting) is performed to find crack patches (or crack candidate regions) and then, a technique is employed to segment the crack pixels in the identified crack patches. The employed technique could be either an IPT or a shallow fully convolutional network (FCN). For the former case, several  
Figure 6: Typical output of a deep learning-based crack detection approach based on the SS setting [74]. Figure 7: Categorization of deep crack detection approaches based on the SS setting. IPTs, such as structured Random Forest edge detection [75], tabularity flow [76], [PERSON]'s thresholding [77], and fast block-wise segmentation [78] have been used. For the latter case, mainly the Mask R-CNN [79] framework which is an extended version of faster R-CNN has been used. Mask R-CNN consists of an extra shallow FCN being utilised to perform SS on the bounding boxes which contain cracks [75; 80; 81]. Several studies have first used classification architectures to find crack patches, and then different IPTs to extract the crack map at the pixel-level. [PERSON] et al. [77] used GoogLeNet [82] and ResNet [33] classifiers to detect crack patches. Then, [PERSON]'s thresholding was employed to segment the identified crack patches, followed by median filtering and the Hessian matrix, which were used to eliminate the influence of illumination and to enhance the crack structures, respectively. In [78], the idea of transfer learning was employed at the crack patch identification step using a pre-trained architecture on the ImageNet data set. Then, fast blockwise segmentation and tensor voting curve detection methods were employed to create the crack mask and improve the accuracy of crack localization, respectively. Faster R-CNN architecture has also been used, combined with IPTs to extract the crack mask, to identify crack regions as the first step. In [83], a Bayesian fusion algorithm was employed to suppress false alarms based on the orientation of the identified crack regions. Then, a set of IPTs, known as morphological operations, were considered to extract the crack mask. [PERSON] et al. [76] proposed to perform crack segmentation in the same manner. However, in their proposed approach, after crack region identification by Faster R-CNN, a modified tabularity flow field, an IPT, was employed to perform crack segmentation on concrete images taken from both indoor and outdoor environments. In the case of approaches in which first, crack patches and regions are identified and then, an FCN is used to perform crack segmentation at the pixel-level, mask R-CNN has been most employed [75; 80; 81; 84]. It is noteworthy to mention that [PERSON] et al. [84] defined a new threshold resulting in a more desirable segmentation of irregular long-thin cracks. [PERSON] et al. [80] employed mask R-CNN to detect concrete bugholes at the pixel-level. In [75], a comprehensive comparison was performed between the application of mask R-CNN and a framework where faster R-CNN combined with structured random forest edge detection was employed for the task of crack segmentation. They proved that mask R-CNN achieves higher accuracy in comparison to that combination for the task of crack segmentation. Three more hybrid SS approaches were found in the literature, where after patch detection, deep architectures have been utilised to output the crack segmentation map. In [85], GoogLeNet [82] was employed for crack patch detection. Then, the detected patches were fed into a feature fusion module and a set of consecutive convolution layers to perform the crack segmentation. [PERSON] et al. [86] proposed a Sobel-edge adaptive sliding window technique to extract crack patches, which is computationally more efficient than the standard sliding window technique.