## 4 Experiment
### Experimental Results
#### 4.4.1 Comparison of Various Methods in Semantic Segmentation  
To test the performance, we used the KITTI road dataset which is widely used for road semantic segmentation research. It comprised 289 training images with a resolution of 1242 \(\times\) 375. We also performed a NTUT sunny dataset, NTUT cloudy dataset, NTUT rainy dataset and NTUT night rainy dataset. Each of them comprised 250 training images with a resolution of 1920 \(\times\) 1080. Figure 12 shows the road segmentation results for the datasets using LWF-VGG FCN. Table 2 shows the performance of the various approaches in the KITTI dataset. Image-based, image + RGB-D based, and image + LiDAR-based approaches had a comparable performance. However, image-based approaches incur a lower equipment cost and the techniques can be more easily realized. To solve different driving scenarios in Taiwan and show the performance of our approach, we used four different datasets for testing. Table 3 shows the performance in different scenarios and a comparison of different approaches. Figure 12 shows the road semantic segmentation results of LWF-VGG FCN for the sunny scenario in the KITTI dataset and NTUT Sunny dataset. The performance in the KITTI dataset of LWF-VGG FCN and LWF-VGG FCN tiny were very close to the modern state of the arts. For the maximum F -score and maximum precision, LWF-VGG FCN had better performance compared to the others. For recall, LWF-VGG FCN tiny had better performance compared to the others. The KITTI dataset is a widely used dataset in the field of road semantic segmentation. However, it's not suitable for Taiwanese road scenarios with various climates. To validate its applicability to road semantic segmentation in Taiwan, the proposed FCNs were tested using a dataset for road conditions in Taipei City, which was collected by the National Taiwan University of Technology (NTUT). The NTUT dataset comprised four sets of images for different weather conditions and times of day: sunny, cloudy, rainy, and night rainy. According to the scenarios named the NTUT sunny dataset, NTUT cloudy dataset, NTUT rainy dataset, and night rainy dataset, respectively,  
Figure 12: Road semantic segmentation results using LWF-VGG FCN. to show the performance of LWF-VGG tiny, we used the state of the art methods called MultiNet [34], BiSeNet [52], and BiSeNet v2 [53] to obtain a comparison. Table 3 shows the performance of LWF-VGG FCN and LWF-VGG FCN tiny compared with different approaches. The NTUT rainy dataset was the most challenging dataset of our driving scenario. Due to the reflection and refraction on the surface, the feature of the road image became complicated. To retain the complicated features of the rainy \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Input** & **Max F1-Score** & **Avg PRE** & **Max PRE** & **REC** \\ \hline VGG16 FCN & Image & 0.9243 & 0.9810 & 1.0000 & 0.8779 \\ StieNet II & Image & 0.9488 & 0.8775 & 0.9297 & 0.9687 \\ MultiNet & Image & 0.9488 & 0.9371 & 0.9484 & 0.9491 \\ RBNet & Image & 0.9497 & 0.9149 & 0.9494 & 0.9501 \\ LidCamNet & Image + LiDAR & 0.9603 & 0.9393 & 0.9623 & 0.9583 \\ NF2 CNN & Image + LiDAR & 0.9670 & 0.8993 & 0.9537 & 0.9807 \\ PSPNet & Image & 0.9629 & 0.9371 & 0.9622 & 0.9635 \\ PLARD+ & Image + LiDAR & 0.9703 & 0.9403 & 0.9719 & 0.9688 \\ SNE-RoadSeg+ & RGB-D & 0.9740 & 0.9401 & 0.9801 & 0.9749 \\ LWF-VGG FCN & Image & 0.9741 & 0.973 & 0.978 & 0.9751 \\ LWF-VGG FCN & Image & 0.9745 & 0.9655 & 1.0000 & 0.9845 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of different neural networks using the KITTI road dataset. Max: maximum; Avg: average; PRE: precision; REC: recall.