## 5 Experimentation and Analysis
### Experiment: Results on the Indian Driving Dataset (IDD)  
To further validate the effectiveness of the model of this paper in rural road segmentation, we selected Unet, Enet, BiSeNetv1, BiSeNetv2, PP-LiteSeg, and the improved PP-LiteSeg model for comparison. All these networks are well-known in the field of lightweight semantic segmentation. The experimental configurations of the other mod \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **MIoU (\%)** & **Kappa (\%)** & **Dice (\%)** & **Parameters** & **FLOPs (G)** &
\begin{tabular}{c} **FPS** \\ (\(\mathbf{f}\cdot\mathbf{s}^{-1}\)) \\ \end{tabular} \\ \hline Unet & 39.32 & 86.83 & 62.31 & \(1.34\times 10^{7}\) & 118.02 & 6.13 \\ Enet & 33.82 & 85.28 & 47.86 & \(3.60\times 10^{5}\) & 2.50 & 28.73 \\ BiSeNetv1 & 50.51 & 88.60 & 65.69 & \(1.29\times 10^{7}\) & 52.76 & 22.73 \\ BiSeNetv2 & 40.68 & 88.83 & 51.25 & \(2.33\times 10^{6}\) & 7.52 & 43.50 \\ PP-LiteSeg & 51.38 & 88.20 & 64.55 & \(8.04\times 10^{6}\) & 5.34 & 41.67 \\ The proposed & 53.82 & 89.36 & 67.04 & \(11.0\times 10^{6}\) & 5.42 & 40.03 \\ method & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of comparison of MIoU (%), Kappa, Dice, and parameters for different models. els are consistent with our approach, and all experiments are performed entirely on the IDD dataset. IDD (Indian Driving Dataset) is a semantic segmentation dataset specifically designed for Indian autonomous driving scenarios, aiming to deal with the complex unstructured road environment in India. As can be seen in Table 4, UNet performs significantly worse with only 28.85% MIoU and 50.20% Dice, showing its limitations in dealing with unstructured environments like rural roads. Although ENet has the lowest number of parameters and FLOPs, its MIoU of 21.82% and Dice of 32.39% are also the lowest, reflecting its shortage in segmentation performance. The MIoU and Dice of BiSeNetv1 are 63.58% and 47.38%, and the MIoU and Dice of BiSeNetv2 are 39.39% and 49.63%, respectively; BiSeNetv2 has a lower accuracy and does not acquire more contextual information, although it is faster in reasoning. The original model has an MIoU of 49.48% and a Dice of 62.59%, which is a good trade-off between speed and accuracy, but again, it does not capture complete feature information. The improved PP-LiteSeg has an MIoU of 51.08%, a Kappa coefficient of 87.81%, and a Dice coefficient of 64.66%, which provides an optimal balance between accuracy and computational efficiency, even though its number of parameters and FLOPs are slightly higher than other lightweight models. As can be seen in Figure 8, the UNet model performs moderately well on unhardened road and sky segmentation, and, due to multiple downsampling, UNet is weaker on detailing, with unhardened roads incorrectly segmented in the first column of the second row, pedestrians unsegmented in the fourth column of the second row, and obfuscated pavements in the second column of the second row. The segmentation results of the ENet model are fuzzy, with poor boundary continuity, and prone to segmentation errors; the asphalt road in the third row and third column is not correctly segmented, and the segmentation of the road and the unhardened pavement is unclear and discontinuous. Two models, BiSeNetv1 and BiSeNetv2, have better segmentation results on larger objects and more regular scenes. However, both models still perform poorly on small objects, and the vehicle in the first column of the fourth row is incorrectly segmented as a roadway. Due to the small receptive field, BiSeNetv1 and BiSeNetv2 fail to effectively capture the contextual information of the image, resulting in rougher segmentation of small objects. The original model performs significantly better than the previous model in complex unstructured scenarios.