## 4 Results and Discussion
### Experiment and Analysis
#### 4.2.3 Ablation Experiment  
In order to test the influence of different modules on the original network model, we designed an ablation experiment to add an adaptive sampling radius module and channel attention mechanism into the initial deep learning network to provide test effects, to verify the effect of each module on segmentation accuracy. Figure 11: Accuracy evaluation analysis plot of the network with the channel attention mechanism module (FC) alone added versus the original Pointnet++. Figure 10: Visualization of segmentation results for the network with the channel attention mechanism module (FC) alone compared to the original Pointnet++. The improvement in segmentation results for the distal sparse point clouds lane lines with the addition of the channel attention mechanism module can be seen in the red dashed box. Based on the above two comparison experiments, both SRC sampling module and channel attention mechanism can positively affect the native deep learning network. Therefore, we added both modules to the original network to generate a fusion network model. We trained and experimented on the MLS road point clouds dataset to verify whether the fusion network generated by adding the two modules has positive enhancement. Figure 12 shows the segmentation results of the native network model and the network model with different modules added under the same conditions and in the same region. The fusion network model's segmentation of distal lane lines is significantly improved compared with the native network model, proving that the fusion network can quickly learn the inhomogeneous sparse MLS road point clouds features and substantially improve the segmentation accuracy of distal lane lines with sparse point clouds. As seen in Figure 13, the guardrail, as a feature bordering the road, does not easily learn the boundary features of the inhomogeneous sparse MLS road point clouds, leading to the failure of the method in the boundary part. The native network identifies part of the guardrail as the road surface and a small part of the boundary as signage. Under the same conditions, the fused network model has significantly improved the segmentation accuracy of guardrails and road surfaces. Most road point clouds data are segmented with clear boundaries and high accuracy, except for a few areas with segmentation errors. Figure 12: Visual comparison of segmentation results for the ablation experiment, the comparison in the red dashed box shows the improvement of the fusion network with the addition of both the adaptive sampling radius and the channel attention mechanism modules on the distal sparse point clouds lane line segmentation results. Figure 13: Visualization results of segmentation results of ablation experiments. Close-up of the road and guardrail edges, with the junction of the road and guardrail edges in the solid red box, and the improvement of the segmentation results of the road scene after adding different modules. Figure 14 shows the performance of the fusion network model with both the SRC module and the channel attention mechanism added compared with the original network and other single-module networks. As shown, we can see the fusion network model generated by adding the two modules can still learn the features of streetlights under the condition of sparse point clouds, which significantly improves the accuracy of streetlight segmentation. Moreover, the segmentation accuracy of all other features has been significantly improved: the single-class IOU of guardrail is improved from 85.1% to 99.9%, the single-class IOU of signage is improved from 83.5% to 98.3%, and the IOU of lane lines is improved from 52.6% to 66.2%. The fusion network model achieved an overall segmentation accuracy of 99.1% for the test area de improved by 2.2% compared with the original network. The mIOU reached 72.6%, improved by 9.3%, proving that the fusion network generated by adding two modules at the same time has excellent segmentation effect on MLS point clouds data and has been effectively improved in all aspects.