## 4 Experiments and Results
### Ablation Experiment  
Ablation experiments are set up to study the impact of individual sub-modules of the triple attention module on network performance. In ablation experiments, three attention modules are pruned and combined to verify the performance of each sub-module. An encoder-decoder backbone network with skip connections and without attention is employed as a baseline model. The networks with only AG, only spatial attention, only channel attention, and channel + spatial attention modules are designed respectively. The results of network segmentation for six different module combinations are shown in Figure 12.  
It can be seen from Figure 12 that all segmentation results with the attention module are better than the results of the backbone network without the attention module. The AG attention module is used to analyze contextual information. It increases the sensitivity of the model to foreground pixels by scaling the attention coefficients to focus the network more on local regions. The effect on the network is shown in the segmentation results, where the lane thickness is more evenly contoured and smoother, with fewer burrs and bumps than other censored combinations. However, additional noise is added due to the increased pixel sensitivity.  
The spatial attention module makes full use of global information and focuses on the regions of interest in the feature map, indicating the distribution of key information on individual channel feature maps. The performance of the spatial attention module  
Figure 12: Visual segmentation results of different attention module combinations.  
in the segmentation results is shown by a significant reduction in noise. However, the segmentation integrity of the spatial attention module is inferior to the combination of AG, channel attention, and spatial + channel attention. The role of channel attention is similar to that of spatial attention. The weights of each channel of the input image are calculated through the network, and the channels containing key information are focused on spatial attention, which guarantees the integrity and continuity improvement of the narrow lines in the segmentation.  
The spatial + channel attention combination combines the characteristics of both attentions to reduce noise while the segmentations maintain integrity. It can be seen from Figure 12, the combination of the three attention modules performs better overall, although a single attention or the combination of any two attentions performs well. The proposed method finally uses a combination of all three attentions. The advantages of each module are complementary. Finally, the proposed method obtains the minimum error of the segmentation to the ground truth. The segmentation evaluation results are shown in Table 3.  
From the segmentation evaluation metrics, the combination with the added attention module significantly outperforms the networks without the attention module. The network with only the spatial attention module performs better than the networks with only one of the other modules. However, the overall segmentation of the triple attention module is better than other combinations. It is shown that the proposed CSTA network has the best segmentation performance.