# PSNet: Parallel-Convolution-Based U-Net for Crack Detection with Self-Gated Attention Block
## 3 Results and Discussion
### Comparison with the State-of-the-Art Methods  
In order to evaluate the performance of our proposed PSNet, we conducted an experiment. The image segmentation accuracy was the main indicator of our measurement. In order to demonstrate the effectiveness of our method, several traditional machine-learning-based models and attention-based convolution models were used as baseline algorithms.  
Before our comparison, we provided some relevant explanations for these mainstream algorithms:  
FCN--a fully convolutional neural network;  
ConvNet--a deep convolutional neural network;  
Split-Attention Network--a channel-wise attention-based network;  
Cascaded Attention DenseUNet--an attention-based network with global attention and core attention;  
ECA-Net--a lightweighted channel attention-based convolutional neural network;  
DWTA-UNet--a U-Net-based network with discrete wavelet transformed image features.  
As shown in Table 1, it can be seen that compared with the mainstream attention-based convolution networks, DWTA-UNet, ECA-Net, Cascaded Attention DenseUNet, and Split-Attention Network, our proposed PSNet can achieve much better segmentation accuracy on all datasets. Thus, it strongly proves the effectiveness of our method, which is the Parallel Convolution Module (PCM) and the Self-Gated Attention Block (SGAB). For this reason, different resolutions of features are maintained through the continuous fusion of four blocks of the PCM, which could avoid information loss caused by downsampling in the CNN model. Additionally, the SGAB could control feature flowing in two dimensions, which could select features related to cracks and suppress those unrelated features. Compared with the U-Net model, it was noticed that attention-based models such as ECA-Net, Cascaded Attention DenseUNet, Split-Attention Network, and DWTA-UNet could acquire better performance since these models use attention blocking to decrease the interference of shadow and lights. Compared with these fully convolutional neural networks such as FCN and ConvNet, the U-Net model can achieve a higher performance since the U-Net model not only has a deeper layer, but also has a feature fusion policy which can achieve more discriminative features. Compared with SVM and CrackForest, FCN can achieve a relatively better result, for the reason that deep learning models can automatically extract high-level features, which is more generalized than handcrafted features.  
Here, we present some examples of our PSNet's detection results for crack images, which were taken from the CRACK500 datasets. These crack images and their detection results are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Cracktree200** & **CRACK500** & **CFD** & **OAD\_CRACK** \\ \hline CrackForest [15] & 0.08 & 0.199 & 0.104 & 0.08 \\ SVM [16] & 0.382 & 0.418 & 0.32 & 0.215 \\ FCN [17] & 0.39 & 0.513 & 0.585 & 0.416 \\ ConvNet [18] & 0.471 & 0.591 & 0.579 & 0.422 \\ U-Net by Jenkins [19] & 0.75 & 0.681 & 0.851 & 0.681 \\ U-Net by Nguyen [20] & 0.763 & 0.695 & 0.856 & 0.683 \\ Split-Attention Network [21] & 0.851 & 0.73 & 0.963 & 0.696 \\ Cascaded Attention & 0.863 & 0.74 & 0.97 & 0.695 \\ DenseUNet [22] & 0.885 & 0.753 & 0.971 & 0.711 \\ DWTA-UNet [24] & 0.90 & 0.77 & 0.973 & 0.729 \\ PSNR [25] & 0.926 & 0.812 & 0.985 & 0.762 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with the state-of-the-art methods.  
Figure 6: Example of the results of our PSNet for detecting crack images.