# DFP-Net: A Crack Segmentation Method Based on a Feature Pyramid Network
## 4 Experiment Results
### Comparison  
To further validate the advantages of DFP-Net on the crack segmentation tasks, we compared it with Deeplabv3, SegNet, U-Net, and FCN-8S, which are classical semantic segmentation networks. It is representative to compare them. When evaluating various segmentation network models for crack localization, we delve into critical observations and comparative analyses. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Branch** & **Recall (\%)** & **PA (\%)** & **IoU (\%)** & **F1 (\%)** \\ \hline primary fusion & 37.85 & 23.00 & 16.79 & 0.29 \\ secondary fusion & 58.10 & 42.29 & 33.86 & 0.51 \\ tertiary fusion & 62.95 & 65.99 & 44.71 & 0.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of each feature fusion. The result of primary fusion contains semantic information of the first two branches. The secondary fusion added information extracted from the third branch based on primary fusion. And the tertiary fusion fused all the semantic information. Figure 6: Comparison of prediction results of the second FPN branches. The size of the three branches outputs are 16 \(\times\) 16, 64 \(\times\) 64, 256 \(\times\) 256, and then we adjust them to the same size for comparison. BiseNet preserves spatial information and high-resolution features by employing global average pooling to capture global information and achieve a sufficient receptive field through downsampling feature learning. However, in complex environments with imbalanced foreground-to-background ratios, the model's ability to capture targets may be compromised. Simultaneously, global average pooling inevitably results in the loss of shallow semantic information. Although the segmentation visualization results reflect crack shapes, the model struggles with crack edge information, as shown in Figure 7e. Nevertheless, its use of average pooling effectively ensures a relatively small computational cost, making it the fastest detection model. ICNet incorporates the pyramid pooling module from PSPNet and reduces the model's inference time through a cascade guidance structure. Despite introducing a cascade feature fusion module to enhance the segmentation performance, the model remains lightweight and is limited in handling tasks with high demands for detail processing. Therefore, the model performs exceptionally well in terms of speed, nearly reaching the detection speed of BiseNet. However, it is evident that such a network structure faces challenges in the feature extraction of finer cracks, only capturing broader areas of cracks, as shown in Figure 7f. The model does not exhibit a strong crack segmentation performance, with all four representative evaluation metrics being the lowest among all of the models. The encoder of FCN adeptly extracts high-level feature information from images through operations such as convolutional layers, playing a pivotal role in tasks like image classification, object detection, and semantic segmentation. While capable of restoring image details and boasting a relatively simple structure, the encoder-decoder architecture gradually forfeits image information due to multiple convolution and pooling operations. This results in the loss of unrecoverable details such as edges and textures, as portrayed in Figure 7c. Although this method identifies robust cracks, it encounters challenges when it comes to recognizing finer cracks, presenting them in a fragmented or missing state. The model's extraction of learned crack features is insufficient, leading to a higher background recognition of cracks, impacting performance its performance in intersections over union metrics despite a superior recall rate. SegNet effectively preserves contours and geometric information, showcasing superior pixel segmentation accuracy among the compared models. However, when dealing with low-resolution feature maps, detailed information tends to be overlooked. As illustrated in Figure 7d, while SegNet segments crack shapes with completeness, the edges are excessively smoothed, rendering the small cracks nearly invisible.