# Shadow Removal for Enhanced Nighttime Driving Scene Generation
## 4 Experiment and Discussion
### Quantitative Results
#### 4.2.3 Secondary Evaluation: Task Performance  
Many studies have employed segmentation models to measure the effectiveness of data augmentation, using Intersection over Union (IoU) as a common metric [21; 22; 23; 24; 25]. In particular, the mean Intersection over Union (mIoU) is widely used to assess model performance by calculating the average IoU across all classes, reflecting how well predicted regions align with ground truth. Typically, images captured in daytime conditions are transformed into adverse weather or nighttime scenarios before training the segmentation model, which is then tested on real-world images to evaluate the IoU.  
In this study, the original Cityscapes images, captured in daytime conditions, were transformed into nighttime scenarios and subsequently used to train a segmentation model. Afterward, the IoU was measured on real-world nighttime images from the ACDC and Dark Zurich datasets. The 'Baseline' column represents a model trained on the original daytime Cityscapes data. The other columns represent the types of I2I Translation models and shadow removal methods applied for nighttime image synthesis. As shown in Table 5, adding shadow removal at the initial stage of the I2I translation pipeline consistently improved the Road IoU, which measures the IoU for the road class, in all cases. The underlined entries in the table indicate the data points with better performance. This finding indicates that the shadow removal process allows for a more accurate representation of nighttime road features.  
Figure 9 visualizes the IoU data from Table 5 as a scatter plot, where the purple samples for each column and the adjacent numbers represent the road class IoU values, while the horizontal line at the center of the plot denotes mIoU values. It can be observed that there is a significant performance improvement for the road class, where shadows are predominantly present.  
Figure 10 presents the segmentation results, with gray areas indicating regions predicted as roads. It is evident that when shadow removal is applied before converting the dataset to nighttime conditions, the misclassification of the sky or buildings as roads is significantly reduced. Thus, the shadow removal step enhances the model's ability to capture the distinctive characteristics of nighttime roads, leading to an improvement in the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test Data**} & \multirow{2}{*}{**IoU**} & \multirow{2}{*}{**Baseline**} & \multicolumn{2}{c}{**Without Shadow**} & \multicolumn{2}{c}{**With Shadow**} \\ \cline{3-6}  & & & \multicolumn{2}{c}{**Removal**} & \multicolumn{2}{c}{**Removal**} (Ours) \\ \cline{3-6}  & & & **TSIT** & **CoMoGAN** & **TSIT** & **CoMoGAN** \\ \hline ACDC & mIoU & 17.87 & 28.43 & 37.45 & 29.09 & 36.47 \\ Night & Road IoU & 51.56 & 67.34 & 81.35 & 73.73 & 83.66 \\ \hline Dark & mIoU & 11.74 & 22.66 & **28.87** & 22.76 & 28.19 \\ Zurich & Road IoU & 38.26 & 47.83 & 64.07 & 57.45 & 66.82 \\ \hline \hline \end{tabular}
\end{table}
Table 5: IoU result of segmentation.  
Figure 9: Statistical visualization of IoU result of segmentation.  
performance of data-driven segmentation models. Each column in Figure 10 represents the dataset used for training.