## 5 Experimentation and Analysis
### Experiment: Comparison with State-of-the-Art Methods  
To verify the effectiveness of the models proposed in this paper for rural road segmentation, Unet, Enet, BiSeNetv1, BiSeNetv2, PP-LiteSeg, and the improved PP-LiteSeg models were selected for comparison. All of these networks are well-known in the field of lightweight semantic segmentation. The experimental configurations of the other models are consistent with our approach, and all experiments are performed entirely on the RDD dataset that we constructed. As can be seen from Table 3, the MIoU and Dice of the model of this paper are 54.23% and 67.56%, respectively, which are 14.50 and 4.73 percentage points higher than that of Unet, mainly because the multiple downsampling operations of Unet lead to a large amount of loss of detail information, and that pavement boundaries of rural roads are often irregular and fuzzy, whereas Unet is unable to efficiently capture such complex boundaries. The main reason is that the multiple downsampling operation of Unet leads to a large amount of detail information loss: 20.00 and 4.08 percentage points higher than Enet, respectively. This is mainly because the Enet model is lighter, and although it possesses a faster inference speed, its feature extraction ability is relatively weak, resulting in less performance than more complex models in complex scenarios. It is 3.31, 0.76 percentage points and 13.14, 0.53 percentage points higher than BiSeNetv1 and BiSeNetv2, respectively, which is mainly because, although the BiSeNet family of models maintains the capture of spatial information while fast reasoning through the bilateral network structure, its small sensory field leads to deficiencies in capturing contextual information. The values are 2.44 and 1.16 percentage points higher than the original model, mainly due to the introduction of the Simple Pyramid Pooling Module with Strip Pooling (SP-SPPM) and the Bottleneck Attention Module (BAM), which allow for a better capturing of global contextual information and an enhanced ability to process details in complex scenarios. The SP-SPPM module enhances the interaction of information in different spatial dimensions through strip pooling, whereas the BAM module improves the model's perception of small objects and details by focusing on information in spatial and channel dimensions. These improvements significantly improve the segmentation accuracy of the model in complex rural road scenarios, making our proposed model outperform the conventional model in both MIoU and Dice coefficients. Although the difference in Kappa coefficients is small, it is still statistically significant, and models with higher Kappa coefficients indicate that the model has better consistency in overall categorization and higher reliability especially when dealing with large-scale categories. As shown in Figure 7, the model proposed in this paper can effectively and accurately segment the semantic segmentation targets in the rural road scene. In contrast, the Unet model performs poorly in small object segmentation due to the loss of many details caused by multiple downsampling and, therefore, also suffers from segmentation errors. For example, the non-hardened pavement in column 1 of Figure 7 is incorrectly segmented, and the people in column 4 are not segmented and recognized. Moreover, the banner in the images in column 2 of Figure 7 also suffers from segmentation confusion. The Enet model suffers from fuzzy segmentation results, poor boundary continuity, and segmentation errors. For example, the asphalt road in the image in row 3 of Figure 7 is not segmented. The Enet model suffers from fuzzy segmentation results, poor boundary continuity, and segmentation errors. For example, in the image in column 3 of Figure 7, the junction of the asphalt road and the non-hardened road is not only discontinuous but also segmented ambiguously. The banner area is also incorrectly recognized because the Enet model does not consider the overall information of the image and has a poor ability to capture image information.