## 5 Experimentation and Analysis
### Experiment: Results on the Indian Driving Dataset (IDD)
Also, FLOPs and FPS are similar to the original model. When these two modules are added to the model at the same time, the MIoU, Kappa, and Dice of the model reached 53.82%, 89.36%, and 64.04%, respectively. At this point, there is a slight increase in FLOPs and some decrease in FPS, but it is also comparable to the original model, and there is also a significant increase in MIoU. These results illustrate that these two modules can enable the model to obtain richer features and more fine-grained predictions for the final results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **MIoU (\%)** & **Kappa (\%)** & **Dice (\%)** & **Parameters** & **FLOPs (G)** & **FPS (\(\mathbf{f}\cdot\mathbf{s}^{-1}\))** \\ \hline Base model & 51.38 & 88.20 & 64.55 & \(8.04\times 10^{6}\) & 5.34 & 41.67 \\ +SP & 53.10 & 88.60 & 66.39 & \(8.16\times 10^{6}\) & 5.38 & 41.17 \\ +SAM & 53.39 & 89.02 & 64.42 & \(8.04\times 10^{6}\) & 5.34 & 41.55 \\ +SP+BAM & 53.82 & 89.36 & 67.04 & \(11.0\times 10^{6}\) & 5.42 & 40.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of semantic segmentation models with different functional unit configurations (%). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Base Model (\%)** & **+SP (\%)** & **+BAM (\%)** & **+SP+BAM (\%)** \\ \hline Building & 82.39 & 82.99 & 81.62 & 84.59 \\ Asphalt road & 89.10 & 89.88 & 90.33 & 91.38 \\ Non-hardened road & 75.15 & 76.88 & 75.53 & 77.48 \\ Sky & 70.44 & 71.56 & 74.63 & 75.07 \\ Barrier & 69.07 & 81.39 & 82.06 & 82.75 \\ Car & 60.16 & 59.86 & 63.54 & 63.11 \\ Tower & 93.35 & 93.73 & 93.20 & 93.58 \\ Pole & 65.99 & 65.23 & 65.47 & 68.65 \\ Vegetation & 98.73 & 98.74 & 98.71 & 98.69 \\ Fence & 68.56 & 70.17 & 70.71 & 75.62 \\ Cement road & 62.89 & 61.45 & 64.72 & 69.44 \\ Motorcycle & 56.37 & 55.78 & 62.44 & 64.26 \\ Agricultural & 85.20 & 76.84 & 85.50 & 86.64 \\ machinery & 70.04 & 71.04 & 72.86 & 74.57 \\ Person & 75.78 & 70.07 & 75.30 & 82.45 \\ Truck & 65.07 & 84.08 & 87.79 & 88.87 \\ Traffic sign & 72.19 & 73.83 & 74.69 & 75.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Category accuracy (%) of semantic segmentation models with different functional unit configurations. As shown in Figure 9, the improved model has a better segmentation effect. The basic model has a poor segmentation effect on small objects, such as the traffic sign in column 2 of Figure 9 and the distant person in column 4, which can be alleviated to a certain extent by adding the SP module. In addition, the truck in column 1 of Figure 9 and the asphalt pavement in column 4 have incorrect segmentation problems, and the scene in Figure 9 can be segmented correctly by adding the BAM module. Buildings in column 3 of Figure 9 and pedestrians in column 4 can be segmented correctly with more continuous boundaries by adding two different modules. By adding the BAM module, the scene can be segmented correctly. By adding two different modules, the buildings in row 3 and the pedestrians in row 4 of Figure 9 can be segmented correctly, and the overall boundary is more continuous. By adding the BAM module, the scene in the figure can be segmented correctly. By adding two different modules, the buildings in column 3 and the pedestrians in column 4 of Figure 9 can be segmented correctly, and the overall boundary is more continuous, which completely takes into account the overall information of the image.