# A Highway Pavement Crack Identification Method Based on an Improved U-Net Model
## 3 Methods  
### Multi-Scale Feature Prediction Fusion  
In deep learning, multiscale networks refer to the use of different scales in the same neural network to process the input data. This approach improves the receptive field of the network to better process the objects in the input image, allowing the network to process objects of different sizes. The receptive field (RF) [35] is the area where the pixels in the input image have an impact on the output of the neural network. In a convolutional neural network, the receptive field of each neuron can be understood as the input area of that neuron. By understanding the receptive field size of a neuron, one can better understand how the neural network understands and represents the input image. The size of the receptive field has a significant impact on the performance of a neural network. Smaller receptive fields typically recognize smaller local features (e.g., edges, corner points, etc. ), while larger receptive fields allow for the recognition of larger features (e.g., objects and scenes). In addition, a larger perceptual field can help the network to understand contextual information better, thus improving its performance. Usually, multiscale networks include two aspects: multiscale input and multiscale feature extraction. Therefore, by using multiscale networks, more comprehensive information can be extracted, which includes both global overall information and local detailed information. Multi-scale feature prediction fusion (MFPF) [36] is a commonly used multi-scale neural network structure that is widely used in computer vision fields such as image classification, target detection, semantic segmentation, etc. The core idea is to predict the output of the features in the model at different scales, and then fuse the predicted results. The main purpose of this section is to combine the multi-scale feature prediction fusion idea with the ordinary U-Net network, improve the original U-Net network into a multi-scale feature fusion U-Net network, and apply it to the pavement crack segmentation. The improved U-Net network retains the original convolutional kernel pool part, with the difference that the intentional fusion prediction is a feature of the middle layer of the U-Net network. In addition, the output of the middle layer is restored to the size of the original  
Figure 2: Principle of the U-Net model. target image by a bilinear interpolation operation before the feature fusion prediction, because the size of the feature map output at each scale is different after the target image is down-sampled by the encoder network. Finally, the feature maps of different scales are channel-stitched to obtain a tensor of a larger dimension. This tensor can then be processed using a convolution operation to obtain the final multi-scale feature prediction fusion output. This preserves the information at different scales and efficiently fuses it to improve the performance and generalization of the model. This approach can improve the perceptual field of the network, obtain contextual information at different scales, and enhance the network's ability to understand and judge the target features. The output of this multi-scale feature prediction fusion can be mathematically expressed as:  
\[Z(x)=Conv([Z_{0},Z_{1},Z_{2},Z_{3},Z_{4}]) \tag{1}\]  
where \(Z(x)\) denotes the output after feature prediction fusion, and \(Z_{0},Z_{1},Z_{2},Z_{3},Z_{4}\) denote the up-sampled output feature maps at different scales. The structure of the U-Net network combined with multi-scale feature prediction fusion is shown in Figure 3, from which it can be seen that the pre-processed pavement crack images enter from the input of the network model, and through the multi-layer convolution operation and down-sampling process of the encoder network, the feature maps at different scales can be obtained, which have different resolutions and semantic information. Usually, the shallower feature maps contain more local information, while the deeper feature maps contain more global information.