## 3 Experiments and Analysis
### Comparison with Other Methods
#### 3.2.2 Inference Efficiency Comparison Experiment  
Table 3 summarizes the number of parameters (PN) and Frames Per Second (FPS) of each model on 512 \(\times\) 512 resolution remote sensing images. The FPS for each model were obtained by performing multiple measurements under the same hardware conditions and averaging the results. From the table, it can be observed that after lightweight improvements to the DeepLabV3+ baseline model, the number of parameters was reduced by 153.13 M, while the prediction speed increased by 31.95 f/s. The PSPNet with MobileNetV2 as the backbone has 46.51 M fewer parameters than CDFFNet, and its inference speed increased by 60.18 f/s. However, CDFFNet outperforms PSPNet in terms of road extraction accuracy. Although HRNet and SegFormer have fewer parameters, HRNet's processing of high-resolution images and the use of the Transformer encoder in SegFormer reduce their inference speeds.  
The following four scatter plots show the relative relationship between the Number of Parameters, FPS, and mIoU for each model on the CHN6_CUG and Massachusetts datasets.  
As shown in Figure 5, on the CHN6_CUG dataset, DeepLabv3+ (208.7 M parameters), and RoadCNN (86.15 M parameters) models, which have a larger number of parameters, perform poorly, with mIoU values 3.57% and 1.69% lower than CDFFNet, respectively. This is because larger models tend to capture detailed information, including noise and outliers, during training on smaller datasets. When the dataset is smaller, the model can memorize the details of the training set but fails to generalize to the validation set. On the larger Massachusetts dataset, RoadCNN, with more parameters, can fully exploit its performance, with the mIoU only differing by 0.37% from CDFFNet. U-Net and HRNet, which have moderate parameter counts, maintain relatively low computational overhead while achieving high performance, with mIoU values differing from CDFFNet by only 0.42% and 0.25%, respectively. However, as analyzed in Figure 6, U-Net and HRNet have lower FPS performance. U-Net employs a multi-layer encoder-decoder structure and incorporates skip connections, gradually reducing the spatial resolution and increasing feature channels in the encoder while using skip connections to combine encoder features and gradually restoring spatial resolution in the decoder. HRNet processes high-resolution and low-resolution feature maps through multiple parallel branches and fuses pixel-wise information at different scales to obtain rich semantic information. The special structure of U-Net and HRNet significantly reduces the inference efficiency and image prediction speed, affecting the application of road extraction in real-time scenarios. PSPNet has the least parameters and the highest inference speed, but its road extraction accuracy is far behind that of CDFFNet. In summary, CDFFNet strikes an excellent balance between road extraction accuracy, model complexity, and inference speed, providing an effective reference for the real-time extraction of road information in engineering applications.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **PSPNet** & **U-Net** & **HRNet** & **SegFormer** & **RoadCNN** & **v3+** & **CDFFNet** \\ \hline
**PN/M** & 9.06 & 94.95 & 36.76 & 52.18 & 86.15 & 208.70 & 55.57 \\
**FPS (f/s)** & 123.54 & 16.95 & 27.74 & 58.55 & 26.69 & 31.41 & 63.36 \\ \hline \hline \end{tabular}
\end{table}
Table 3: PN and FPS Metrics of Each Model.