# A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
## 2 Automatic Feature Extraction
### Semantic Segmentation
#### 2.3.2 Pure Semantic Segmentation
In this study, an FCN-based approach through the fine-tuning of the DenseNet-121 architecture was considered to perform multiple damage detection in a challenging data set. In addition to the crack images, the data set includes spalling, efflorescence, and hole images captured using a smartphone in different illumination conditions. They proved that FC-DenseNet outperforms SegNet architecture for the task of crack segmentation. An approach called \"DenseCrack\" was proposed by [PERSON] and [PERSON] [116] where an encoder-decoder structure based on dense blocks is combined with a depth-first search (DFS)-based algorithm as a refinement module. This study also can be considered as an ablation study because three architectures of DenseCrack121, 169, and 201 were employed and compared. In [117], the \"ConnCrack\" approach, which is based on GANs, was proposed. DenseNet121 architecture was employed as the generator of conditional Wasserstein GAN (cWGAN) in this study. Another approach based on the application of FC-DenseNet was proposed by the same research group in [74]. In this study, to further improve the accuracy of the SS, a new loss function based on the connectivity of the pixels in the crack areas is defined. The application of the new loss function benefits the crack segmentation performance both by dealing with the class imbalance challenge and taking into consideration the connectivity of crack pixels. They made a comprehensive comparative study between the proposed approach and different published state-of-the-art crack segmentation approaches on three different data sets. Several other studies have been conducted in the crack detection area with an application of encoder-decoder structures. However, the proposed approaches are not inspired by well-known architectures in the computer vision area. In these studies, the technique that is employed to let the contextual information flow between the encoder and the decoder module is notable. In [PERSON] et al. [118], the up-sampling part is the core novelty of the proposed architecture. The up-sampling part combines global information and local information by adding specific convolutional and deconvolutional layers resulting in the ability of the proposed architecture to deal with multi-scale and multi-level images. In [119], the ideas of transfer learning and dilated convolution before the decoder module are combined to perform crack detection at the pixel-level. Dilated convolution (aka atrous convolution) was originally designed to aggregate contextual information at multi-scales for SS [120]. Another approach called \"DeepCrack\" was proposed by [PERSON] et al. [121]. In this study, to let the contextual information flow, side-output layers are inserted after convolutional layers. After deep supervision at each side-output layer, the outputs are concatenated to form a final output layer that acquires multi-scale and multi-level features. Then, the fused prediction is refined by conditional random field (CRF) and Guided Filtering (GF) modules to improve the accuracy of SS. The same technique of considering a side network including \(1\times 1\) convolution layers applied at each level of information and associated deconvolution layers was employed by [PERSON] et al. [2]. However, they noted that the side-outputs of low-level layers are messy which stems from lacking context information. To solve this issue, the authors utilised a feature pyramid module between the backbone architecture and the side network resulting in combining multi-scale context information into low-level feature maps. They comprehensively compared the performance of their proposed approach with state-of-the-art deep learning-based crack segmentation approaches over five different data sets. On the other hand, without considering any technique to deal with the fusing of feature maps at different scales, simple encoder-decoder structures were considered in [73; 122].