## 5 Experimentation and Analysis
### Experiment: Results on the Indian Driving Dataset (IDD)
In particular, the segmentation of smaller objects has been improved, but the segmentation is still not detailed enough in some places where the boundaries are more complex, for example, the segmentation of the motorcycle and the pole in the sixth row and fifth column is more ambiguous. The improved PP-LiteSeg model performed the best of all models, segmenting small objects and complex boundaries better. The truck in the second column of the seventh row was segmented correctly, and the pole, traffic sign, and motorcycle in the fifth column of the seventh row were more accurate. The main reason is the enhanced ability to capture global contextual information through the SP-SPPM and BAM modules, making it better able to handle details in complex scenes. \begin{table}
\begin{tabular}{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 & 28.85 & 85.66 & 50.20 & \(1.34\times 10^{7}\) & 118.02 & 6.56 \\ Enet & 21.82 & 84.37 & 32.39 & \(3.60\times 10^{5}\) & 2.50 & 25.17 \\ BiSeNetv1 & 47.38 & 87.23 & 63.58 & \(1.29\times 10^{7}\) & 52.76 & 17.87 \\ BiSeNetv2 & 39.39 & 87.34 & 49.63 & \(2.33\times 10^{6}\) & 7.52 & 35.26 \\ PP-LiteSeg & 49.48 & 87.22 & 62.59 & \(8.04\times 10^{6}\) & 5.34 & 39.20 \\ The proposed method & 51.08 & 87.81 & 64.66 & \(11.0\times 10^{6}\) & 5.42 & 38.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of comparison of MIoU (%), Kappa, Dice, and parameters for different models. Ablation Experiment: Category Accuracy of Semantic Segmentation Models with Different Functional Unit Configurations  
Ablation experiments were conducted to validate the effectiveness of the proposed modules and analyze the impact of each module on the model performance. Based on the original model, the SP and BAM modules are introduced step by step. The performance of the model was analyzed by evaluating metrics such as single-class pixel accuracy, MIoU, Kappa, and Dice, as well as considering the number of parameters of the model. Tables 4 and 5 show the results of the model run on the test set. Figure 8: Exemplary visual results from various models: row (**a**) illustrates the input images, (**b**–**f**) display the segmentation outcomes of UNet, ENet, BiSeNetv1, BiSeNetv2, PP-LiteSeg, and (**g**) displays the results of our approach. As can be seen from Table 4, buildings, asphalt roads, towers, vegetation, agricultural machinery, and other such objects with precise shape, color, and outline features have a higher accuracy rate of segmentation, but for cars, fences, cement roads, and so on, such objects are affected by distance and distribution and therefore have a lower accuracy rate compared to the previous categories. Due to the small area of the motorbike and the poles in the image and the low resolution of the processed image, the resolution of the feature map is further reduced by multiple downsampling operations during the training process, which results in the loss of much more detailed information. Sampling on the images becomes more difficult, and, therefore, incomplete or mis-segmentation occurs in the final result, especially for these categories, which tend to be less accurate. As can be seen from Table 6, after adding only the BAM module, the MIoU, Kappa, and Dice of the model reached, respectively, 53.39%, 89.02%, and 64.42%. This shows that the BAM module can capture more spatial position information to some extent, which can improve the prediction performance of the model. There was no significant increase in FLOPs, and FPS was similar to the original model. Adding the SP module to the Simple Pyramid Pooling module resulted in model MIoU, Kappa, and Dice of 53.10%, 88.60%, and 66.39%, respectively. It shows that extracting the features of the strip region is effective, and the enhancement of the model segmentation results is significant when the information from different areas is brought together.