## 3 Results
### Improved Algorithm and Model Constructing  
#### 3.1.1 Other Deep Learning Methods  
For a more intuitive comparison, we briefly introduce the other two methods. Improved U-Net Method  
Parallel convolution module introducing cavity convolution. Set the size of the characteristic graph of the input parallel convolution module as M \(\times\) N \(\times\) K. The first branch road passes through 3 \(\times\) 3 after ordinary convolution (Padding = Same); an M \(\times\) N \(\times\) K/4 feature map is obtained, which focuses on local information near the center point. The second branch passes 3 with a rate of 3 \(\times\) 3. After hole convolution (Padding = Same), an M \(\times\) N \(\times\) K/4 feature map is obtained, which focuses on global information. Then, stack the two outputs to get an M \(\times\) N \(\times\) K/2 feature map, complete the fusion of local information and global information, and help improve the accuracy of crack segmentation. After the convolution of the parallel convolution module, the ReLU activation function of the modified linear unit is added to improve the expression ability of the network. In addition, a batch normalization layer BN is added between the convolution and ReLU activation functions in the U-Net network to speed up the network training speed, enable the network to converge faster, and prevent the network from overfitting. The attention mechanism is similar to visual attention, allowing the network to focus on the target in the image and ignore redundant information. SENet is a simple and effective channel attention mechanism with low complexity and a small amount of computation. It improves the network segmentation effect by changing the weight of the feature map channel. SENet includes three parts: squeeze, exception, and channel weighting. In order to reduce the impact of background and noise and improve the feature recovery capability of the decoder, the parallel convolution module is integrated with the SENet attention mechanism, the channel relationship of the dual scale information feature map is adjusted, the weight of the channel containing the crack feature information is increased, the weight of other channels is reduced, and the weight distribution of features such as the crack edge and shape is strengthened, so as to capture more important semantic information. It can better realize the detection of concrete cracks under a complex background [79]. The Mask-RCNN algorithm is composed of a basic network, region candidate network (region proposal network (RPN)), classification network, and Mask partition network. The basic network is composed of a residual network that removes the full connection layer and uses feature pyramid networks (FPNs) to resample images, fuse the two feature maps, and output feature maps with different depths, so that the feature map has more complete image information for subsequent operations. The purpose of RPN is to use the trained parameters in the generated candidate box to provide a candidate region alignment layer of interest for the classification network (Region of Interest Align, RoIAlign). The input of RPN is the feature map after feature extraction, and many candidate boxes are generated on the feature map. RPN first realizes binary category judgment and boundary box prediction for candidate boxes and then uses the nonmaximum suppression method to filter candidate boxes, reducing invalid candidate boxes and leaving valid candidate boxes, so as to improve the efficiency of the classification network and regression tasks to be carried out later. Compared with RoIpooling, Ro IAlign cancels quantization operation, thus reducing the impact of dislocation caused by quantization operation on algorithm accuracy. The function of the classification network is to predict the category of detected targets within the RoIAlign range and adjust the position of RoIAlign to reduce the impact on algorithm accuracy caused by the inaccurate position of RoIAlign.