# Daydirex: Translating Nighttime Scenes towards Daytime Driving Experience at Night
## 5 Evaluation
### Result  
Figure 13 shows FID scores of translated images for the three cases by our proposed method, Daydriex, and CycleGAN. Daydriex shows lower FID scores for all the cases, which means translated images by our method are better than those by CycleGAN. For the first case, i.e., Rural (slow), the FIDs of CycleGAN and Daydriex are 232.12 and 229.06, respectively. For the second case, Rural (fast), they are 223.18 and 213.58, respectively. The third case shows the largest difference among the three cases; the FIDs are 248.21 (CycleGAN) and 222.15 (Daydriex), respectively.  
Moreover, Figure 14 shows that our proposed method achieves the higher ratio of positive responses (over 4) than CycleGAN for all three cases. Our results get 60%, 80%, and 57% of positive responses for the slow rural, fast rural, and urban case, respectively. In contrast, the CycleGAN results only get 17%, 13%, and 10% of positive responses for those cases. Figure 15 shows the box plots of the Likert scale responses for the three cases. For the slow rural and urban cases, the medians of CycleGAN and ours were 2 and 5, respectively. For the fast rural case, they were 2 and 6, respectively.  
Figure 12: Examples of captured nighttime images (**a**,**d**,**g**), translated images by CycleGAN (**b**,**e**,**h**), and translated images by Daydriex (**c**,**f**,**i**). (**b**,**c**), (**e**,**f**), and (**h**,**i**) are the translations for (**a**), (**d**), and (**g**), respectively.  
Figure 14: Diverging stacked bar charts of the Likert scale responses for the three cases. Daydirex gets more positive responses regarding the perceptual quality of translated images.  
Figure 13: Fréchet Inception Distance (FID) for the three cases. Daydirex shows lower FID scores for all the cases.  
To evaluate the difference in the responses, we ran a Wilcoxon Signed-rank test. We found that there is a significant difference for the all three cases: the slow rural case (W = 4, Z = \(-\)4.73, \(p\) < 0.05, r = 0.61), the fast rural case (W = 0, Z = \(-\)4.81, \(p\) < 0.05, r = 0.62), and the urban case (W = 27, Z = \(-\)4.13, \(p\) < 0.05, r = 0.53). From these results, we can see that the results translated by our proposed method were perceived more natural daytime scenes.  
Table 5 shows the result of the average computation time required for the main processing stages. The time varies depending on the stages. Please note that the result for the hint image retrieval stage only includes the time of operations needed to generate a panoramic image without the time taken to download an image from the street view service. Although the hint image retrieval takes the longest time, 710 ms, the position mating, perspective matching, and image merging stages take about 9 ms, 2 ms, and 0.02 ms, respectively. The size of front and side images used to construct panoramic images is 1280 \(\times\) 720 pixels. In the case of image translation, the computation time taken is different depending on the image resolution. It takes 6 ms, 14 ms, and 53 ms at the resolutions of 128 \(\times\) 128, 256 \(\times\) 256, and 512 \(\times\) 512, respectively. Please note that we target these resolutions for image translation since those are mainly used in the previous image-to-image translation works including CycleGAN.