# A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
## 2 Automatic Feature Extraction
### Semantic Segmentation
#### 2.3.2 Pure Semantic Segmentation
Three U-Net architectures with different depths and number of layers were utilised and compared to perform crack segmentation on challenging data sets of CFD and Aigle-RN. In another study [106], to cope with the class imbalance challenge in the crack detection area, the focal loss function was employed for training of the U-Net architecture to ensure higher generalization performance. [PERSON] et al. [107] proposed a modified version of U-Net where two modules are added to the architecture to boost the performance of crack detection. The multi-dilation module equipped with dilation convolution and hierarchical feature learning module are the added modules which are responsible for obtaining crack features of multiple context sizes and deriving multi-scale features from high- to low-level convolution layers, respectively [107]. [PERSON] et al. [108] asserted that in industrial pixel-level crack detection, the aforementioned challenge results in a common problem called \"All Black\". \"All Black\" happens when the algorithm classifies all the pixels of the pavement image as the background and still achieves good accuracy. To solve this problem, they proposed an approach called CrackGAN, which is based on generative adversarial networks (GANs) [109]. In this approach, the asymmetric U-Net is employed as the backbone architecture of the generator network providing the ability to work with images of arbitrary sizes. **SegNet** is an encoder-decoder architecture where the encoder is the VGG-16 [38] architecture without the fully connected layers [87]. The main feature of SegNet is that during the decoding, the max-pooling indices at the corresponding encoder layer are recalled and used to up-sample in the decoder module. This makes SegNet faster than U-Net. A pavement and bridge crack segmentation network inspired by SegNet architecture was proposed by [PERSON] et al. [110]. To train the end-to-end deep learning model proposed in this study, the \"AdaDelta\" optimizer and the cross-entropy loss function were used. SegNet architecture was employed and modified to have a better performance on line like object segmentation in the area of crack detection by [PERSON] et al. [111]. The proposed approach in this study is called \"DeepCrack\" and feature fusion at different scales was considered as the main novelty. Particularly, they fused the sparse features at smaller scales with continuous features at larger scales to have a better performance for the task of crack segmentation. It is stated in [112] that the proposed encoder-decoder structure in the study which is inspired by SegNet [87], FCN [89], and ZFNet [113] was the first application of deep learning to find cracks in black-box images. A pre-trained ResNet [33] on the ImageNet data set was employed as the encoder module. Also, in the decoder module, the deconvolution technique of ZFNet and SegNet, which is based on storing the location information in the max-pooling layers and boundary information in the encoder feature maps, was considered in the three deconvolutional layers. **DenseNet** was first proposed for the task of IC as an improvement to the ResNet [33] architecture [114]. ResNet architecture is involved with a large number of parameters because each layer has its weights and it has been proven that many layers in the ResNet architecture contribute very little. DenseNet architecture is proposed as a solution to this challenge. The main feature of the DenseNet architecture is the application of dense blocks, which benefits the task by alleviating the vanishing gradient problem, improving feature reuse, and significantly decreasing the number of parameters. Later the same year, the FCN version of DenseNet known as FC-DenseNet was proposed by [PERSON] et al. [100] for the task of SS. To the best of the authors' knowledge, the first application of FC-DenseNet for the task of crack segmentation was done by [PERSON] et al. [115].