# DFP-Net: A Crack Segmentation Method Based on a Feature Pyramid Network
## 2 Related Work
### Image Segmentation  
To address the limitation of pixel-level accuracy in target detection when dealing with cracks in concrete buildings and achieve more precise crack identification and localization, the intelligent identification field of concrete building cracks has incorporated the semantic segmentation method. By using semantic segmentation, each pixel in the image can be divided more meticulously to provide more accurate crack boundary information, effectively solving the problem of object detection not being able to obtain crack pixel positions.  
In 2019, [PERSON] et al. [54] combined U-Net and full convolution in the semantic segmentation task of cracks, introduced residual modules to optimize the network structure, and added attention mechanisms. Also, in the same year, [PERSON] [55] use VGG16 as the backbone network to extract target features and construct a network structure based on deep fully convolutional networks to obtain sufficient sample context information. This method can effectively achieve crack localization and segmentation.  
Figure 1: The detection effect of improved YOLOv3 on the crack data set. The red box marks a part of the crack, and the position of the crack is somewhat clear through a large number of small marking boxes.  
In 2021, [PERSON] et al. [56] designed a semi-supervised segmentation model to reduce the workload of sample labeling in dataset preparation. This model extracts crack features using EfficientUNet and introduces a teacher-student network to improve the detection efficiency of cracks while ensuring the segmentation accuracy of the model. Also, in the same year, [PERSON] et al. [57] have made improvements to DeeplabV3+. By introducing spatial pyramid pooling, the receptive field of the convolutional layer is expanded while reducing the computational parameters of the model. This allows the model to extract more global semantic information while minimizing the size of the model. In 2022, [PERSON] et al. [58] improved the input sample resolution through super-resolution reconstruction and achieved quantification of cracks.  
At present, the crack identification method based on semantic segmentation has made remarkable progress in improving pixel-level accuracy. These methods successfully locate cracks in images pixel by pixel. However, currently, existing methods still face some problems. For example, current methods may have leak detection problems for small or very small cracks. In addition, there is still room to improve the robustness of the model when dealing with complex scenes, light changes, etc. In order to alleviate these problems, a method more adaptable to different crack types and scenarios is needed, and will achieve a more comprehensive and accurate identification of cracks.  
In this paper, the proposed work Double Feature Pyramid Network (DFP-Net) mainly makes the following three contributions.  
(i) First, the cascade image input mechanism (i.e., low, medium, full, and high resolution) is utilized in the six feature branches which extract different scale features, and the input image with a higher resolution corresponds to the feature extract branch, which extracts lower-dimension semantic information.  
(ii) Then, the inner feature pyramid is established in the input image with the highest resolution to obtain copious shallow semantic information; meanwhile, the outer feature pyramid is constructed as a three-resolution image to extract multi-dimension semantic information.  
(iii) Additionally, we introduce a Cascade Feature Fusion unit (CFF) designed to sequentially fuse feature maps from cascade branches with varying resolutions. This innovative unit aims to enhance the integration of global semantic information.