## 3 Results
### Ablation Experiments  
To substantiate the contributions of structural optimization, attention mechanisms, data augmentation methods, dilated convolutions, and feature transfer methods to the outcomes of indoor path extraction, this chapter selects the image exhibiting the most severe overfitting phenomenon in the WIPD for ablation experiments. These experiments compare results before and after model enhancements based on the proposed improvement ideas. Additionally, effect maps, based on the U-Net model structure, are included for comparison to showcase structural optimization. Here, \"3 layers\" denotes a variant of the U-Net structure after initial optimization, as illustrated in Figure 1.  
The results of the ablation experiments reveal notable trends. In Figure 7c,d, the effects were unsatisfactory prior to the implementation of the feature transfer strategy. Following the addition of dilated convolutions, as depicted in Figure 7e, although the receptive field expanded, adjusting the model's fitting direction remained challenging, resulting in misclassifications of walls as paths and the central area's paths as background. Figure 7f,g illustrate that the spatial self-attention mechanism substantially enhanced path segmentation, enabling the segmentation of paths with stronger connectivity and intact edges. However, overfitting occurred, leading to misclassifications of walls and obstacle surfaces. The inclusion of data augmentation and other enhancements, as seen in Figure 7h, exacerbated the overfitting phenomenon of the U-Net model. Finally, the last image, Figure 7i, demonstrates the effectiveness of IndoorPathNet in extracting detailed and intact path semantic maps from BEV indoor storage images. Figure 8 shows the training metrics of IndoorPathNet on the WIPD.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multicolumn{2}{c}{**(a)**} & \multicolumn{2}{c}{**(b)**} & \multicolumn{2}{c}{**(c)**} \\ \cline{2-9}  & **P** & **R** & **IoU** & **P** & **R** & **IoU** & **P** & **R** & **IoU** \\ \hline FCN-8s & 0.591 & 0.654 & 0.451 & 0.588 & 0.612 & 0.503 & 0.657 & 0.628 & 0.491 \\ FCN-32s & 0.553 & 0.611 & 0.409 & 0.529 & 0.574 & 0.480 & 0.563 & 0.623 & 0.510 \\ SegNet & 0.710 & 0.735 & 0.622 & 0.698 & 0.817 & 0.810 & 0.693 & 0.612 & 0.541 \\ U-Net & 0.822 & 0.895 & 0.762 & 0.872 & 0.890 & 0.759 & 0.610 & 0.533 & 0.632 \\ IndoorPathNet & 0.866 & 0.912 & 0.879 & 0.903 & 0.926 & 0.854 & 0.840 & 0.821 & 0.721 \\ \hline \hline \end{tabular}
\end{table}
Table 1: A numerical comparison in the task scenario between current mainstream models and the proposed model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **P** & **R** & **IoU** \\ \hline FCN-8s & 0.573 & 0.631 & 0.497 \\ FCN-32s & 0.602 & 0.681 & 0.503 \\ SegNet & 0.797 & 0.758 & 0.683 \\ U-Net & 0.813 & 0.802 & 0.764 \\ IndoorPathNet & 0.893 & 0.875 & 0.822 \\ \hline \hline \end{tabular}
\end{table}
Table 2: A comparison of whole UAVID between current mainstream models and the proposed model.  
Figure 7: Results of ablation experiments in WIPD: (**a,b**) represent the image and mask, respectively; (**c**) represents the prediction of the three-layer model alone; (**d**) represents the prediction of U-Net alone; (**e**) represents three layers + dilated convolution + feature transfer; (**f**) represents three layers + dilated convolution + spatial self-attention mechanism + feature transfer; (**g**) represents U-Net + dilated convolution + spatial self-attention mechanism + feature transfer; (**h**) represents U-Net + dilated convolution + spatial self-attention mechanism + feature transfer + data augmentation; and (**i**) represents IndoorPathNet + feature transfer + data augmentation.