# A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
## 2 Automatic Feature Extraction
### Semantic Segmentation
#### 2.3.2 Pure Semantic Segmentation
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref** & **Novelty/Novelties** & **Method** \\ \hline
[91] & The first application of deep learning for the task of crack segmentation, where using ConvNet, the feature extraction is done on raw images. & Centre crack pixels in the patches \\ \hline
[94] & Proposing an improved version of CrackNet called CrackNet II resulting in higher performance in terms of accuracy and speed. & Consecutive conv layers with an invariant spatial size \\ \hline
[118] & 1. Application of an encoder–decoder structure to perform crack segmentation without employing the sliding windows technique. 2. Extracting the geometric characteristic of cracks via the application of morphological operations. & Encoder–decoder (feature fusion) \\ \hline
[102] & The first application of U-Net architecture in the crack detection area to cope with several limitations of applying CNN for the task of crack detection. & Encoder–decoder (U-Net) \\ \hline
[103] & Application of U-Net architecture equipped with a new proposed loss function based on distance transform to perform the task of crack segmentation. & Encoder–decoder (U-Net) \\ \hline
[92] & 1. Predicting the crack structure using CNN. 2. Proposing a strategy to deal with the class imbalance challenge. & Centre crack pixels in the patches \\ \hline
[97] & Proposing an improved version of CrackNet called CrackNet-R based on RNN, including a new recurrent unit. & RNN \\ \hline
[122] & Investigation of the performance of FCN architectures in the crack detection area for the task of crack segmentation. & Encoder–decoder \\ \hline
[104] & Proposing an encoder–decoder structure based on U-Net architecture combined with attention gating and residual connections to improve the performance. & Encoder–decoder (U-Net) \\ \hline
[105] & Investigating the effect of the depth of the U-Net architecture on the crack segmentation performance. & Encoder–decoder (U-Net) \\ \hline
[111] & Designing a new end-to-end trainable neural network based on SegNet architecture for robust crack detection. & Encoder–decoder (SegNet) \\ \hline
[115] & Proposing a deeper and more comprehensive FCN architecture to detect four concrete damages where it requires no sliding window technique. & Encoder–decoder (FC-DenseNet) \\ \hline
[112] & Performing a successful application of deep learning methods for detecting road cracks in black-box images. & Encoder–decoder (ResNet + SegNet, FCN, ZFNet) \\ \hline
[73] & 1. Proving the superiority of the SS over OR setting. 2. Proposing a method based on Gaussian kernel and Brownian motion process to generate arbitrary simulated crack images. & Encoder–decoder \\ \hline
[119] & Application of an FCN architecture based on dilated convolution to perform SS of crack images. & Encoder–decoder (dilated convolution) \\ \hline
[121] & 1. Proposing an FCN combined with condition random field and guided filtering methods. & Encoder–decoder (feature fusion) \\ \hline
[106] & Application of U-Net architecture for crack segmentation where Adam algorithm and focal loss function are used as the optimizer and evaluation function, respectively. & Encoder–decoder (U-Net) \\ \hline
[2] & Proposing a feature pyramid and hierarchical boosting network to achieve more robust feature representation and deal with the class imbalance challenge. & Encoder–decoder (feature fusion) \\ \hline
[74] & 1. Proposing a novel deep architecture based on dense blocks. 2. Proposing a novel loss function based on the connectivity of crack pixels in the crack areas. & Encoder–decoder (FC-DenseNet) \\ \hline
[108] & Proposing a CrackGAN framework based on the application of GAN architecture where the proposed approach is capable of working with partially annotated ground truths. & Encoder–decoder (U-Net as generator) \\ \hline
[117] & Proposing a crack segmentation approach based on conditional Wasserstein GAN combined with connectivity map to refine the results. & Encoder–decoder (FC-DenseNet as generator) \\ \hline
[116] & 1. Application of skip connections in the deep architecture for feature fusion. 2. Application of a depth-first search algorithm for post-processing and improving the accuracy. & Encoder–decoder (FC-DenseNet) \\ \hline
[110] & Application of SegNet architecture to perform the crack segmentation where the “Adadelta” optimizer and cross-entropy loss function are used & Encoder–decoder (SegNet) \\ \hline
[95] & 1. Proposing an improved version of CrackNet called CrackNet-V resulting in higher performance in terms of accuracy and speed. 2. Proposing a new activation function to improve the accuracy of crack segmentation for shallow cracks. & Consecutive conv layers with an invariant spatial size \\ \hline
[107] & Proposing U-hierarchical dilated network, a modified encoder–decoder architecture based on U-Net, where two modules of multi-dilation and hierarchical feature learning are added to improve the performance of crack segmentation. & Encoder–decoder (U-Net) \\ \hline \hline \end{tabular}
\end{table}