# A Highway Pavement Crack Identification Method Based on an Improved U-Net Model
## 3 Methods
### New Parallel Attention Module  
Although the U-Net network used in this paper is based on the encoder-decoder structure, which has great advantages in image semantic segmentation, the encoder-decoder  
Figure 4: U-Net network bottleneck layer structure. Figure 5: Normalized U-Net network bottleneck layer design. structure will focus too much on the local information in the image when extracting features from the target image, and thus cannot take into account the global information of the image well, resulting in insufficient accuracy of the final segmentation results. This paper proposed an attention mechanism module that is connected to the output of the multi-scale feature prediction fusion U-Net network, and the role of this module is to obtain richer global contextual information about the target image. The attention mechanism module proposed in this section was improved based on the parallel attention module in the dual-attention network (DANet) [40], and the structure of the parallel attention module in DANet is shown in Figure 6, from which it can be seen that this attention mechanism module is composed of a spatial attention module and a channel attention module by parallelism. The feature maps passing through this attention mechanism module will be processed by the spatial attention and channel attention mechanisms, separately, and the channel descriptions of the two sets of feature maps will be obtained after processing, and then these two sets of channel descriptions will be summed up. The parallel attention module consists of a parallel channel attention module (CA) and a position attention module (PA), where the PA module emphasizes the position dependency between any two different positional features in the image and the CA module is used to focus on the channel dependency between different channels of the image. By summing the outputs of the two attention modules, similar features of subtle objects were selectively aggregated to highlight their feature representations, while reducing the influence of salient objects on image segmentation, and similar features at any scale were adaptively integrated from a global perspective to improve the segmentation accuracy of the images. Figure 6: Parallel attention module structure. In order to enable the network model to associate more global features as well as to fully relate contextual relationships so that more global contextual relationships can be encoded as local features, this paper made modifications based on the parallel attention module by keeping the original SA module and replacing the gated attention mechanism module (GCT Block) with the CA module. A new parallel attention module was constructed for processing the output of multi-scale feature prediction fusion U-Net to further explore the global contextual information of images. The GCT module is also a channel attention module, which enables more efficient global context modeling of feature fusion maps and is more lightweight with fewer parameters than the common PA module. The structure of the proposed new parallel attention module is shown in Figure 7.  
The CA module adjusts the channel features of the feature map by calculating the importance weights of each channel to improve the perception of key features. Specifically, each channel of the feature map is first downscaled by a global pooling layer, then two fully connected layers are passed to obtain the channel weight coefficients, and finally the weight coefficients are applied to the original feature map to obtain the channel attention-adjusted feature map. The GCT module is also a channel attention module. The structure diagram of the gated channel conversion attention module is shown in Figure 8.  
Figure 7: New parallel attention module. Here, \(X\in R^{C\times H\times W}\) is the activation feature in the convolutional network, where \(H\) and \(W\) are the spatial height and width, and \(C\) is the number of channels.