# Shadow Removal for Enhanced Nighttime Driving Scene Generation
## 4 Experiment and Discussion
### Quantitative Results
#### 4.2.2 Primary Evaluation: Image Quality  
Before presenting the experimental results, it is important to introduce the Frechet Inception Distance (FID) [62], a widely used metric for evaluating the quality of generative models. FID compares the statistical properties (mean and covariance) of real-world images and generated images, treating them as multidimensional Gaussians. The distance between these two distributions, known as the Frechet distance or Wasserstein-2 distance, provides a measure of similarity between the real and generated data. The FID score is computed by extracting features using the InceptionV3 network, a pretrained convolutional neural network commonly employed for image classification tasks, which ensures that the extracted features are representative of high-level image characteristics. FID is consistent with human judgment and effectively captures the level of distortion in generated images. In our experiments, FID was used to assess the performance of models across various datasets, highlighting the impact of our methods.  
Table 4 demonstrates that the majority of FID scores improved when shadow removal was applied. The lower the FID, the better the results. The underlined entries in the table indicate the data points with better performance. The 'Baseline' column represents the original Cityscapes data in daytime scenarios, serving as a reference point for comparison with nighttime translation. In experiments utilizing CoMoGAN, FID improvements were observed across all datasets, including ACDC-night, Dark-Zurich, and BDD100k. Similarly, in experiments applying TSIT, the FID scores improved for the ACDC-night and Dark-Zurich datasets. However, there was a performance decline in the TSIT-BDD100k case.  
Although shadow removal can create nighttime images that appear more natural to the human eye, the FID metric, which is based on measuring feature distributions via the InceptionV3 model, might not match the tendency. Hence, to more accurately assess the effects of nighttime translation-based data augmentation, we trained the image recognition model on these synthesized data, and the results can be found in Section 4.2.3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test Data**} & \multirow{2}{*}{**Baseline**} & \multicolumn{2}{c}{**Without**} & \multicolumn{2}{c}{**With Shadow Removal**} \\ \cline{3-6}  & & \multicolumn{2}{c}{**Shadow Removal**} & \multicolumn{2}{c}{**(Ours)**} \\ \cline{3-6}  & & **TSIT** & **CoMoGAN** & **TSIT** & **CoMoGAN** \\ \hline ACDC-Night & 138.54 & 68.93 & 84.50 & **67.93** & **84.43** \\ Dark-Zurich & 209.96 & 141.70 & 138.59 & **140.17** & **137.58** \\ BDD100k & 155.46 & **64.05** & 87.21 & 69.40 & **85.51** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental result of image quality by Fréchet Inception Distance (FID) score.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Segmentation**} & \multicolumn{2}{c}{**FID**} \\ \hline \multirow{3}{*}{**Training (Cityscapes)**} & Day & 2241 & \multirow{3}{*}{**Dataset 1**} & Day & 2241 \\ \cline{2-5}  & Night & 2241 & & & Night & 2241 \\ \cline{2-5}  & Shadow-Free & & & & Shadow-Free & 2241 \\ \cline{2-5}  & Night & 2241 & & & Night & 2241 \\ \hline \multirow{3}{*}{**Test**} & Dark-Zurich & 50 & & & Dark-Zurich & 50 \\ \cline{2-5}  & \multirow{3}{*}{ACDC-Night} & \multirow{3}{*}{506} & \multirow{3}{*}{**Dataset 2**} & ACDC-Night & \multirow{3}{*}{506} \\ \cline{3-6}  & & & & BDD100k & 3267 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset overview.