## 4 Experiment and Results
### Result on MICC-SRI Dataset
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Method**} & **Pixel** & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**F1**} & \multirow{2}{*}{**mIOU**} & \multirow{2}{*}{**FPS**} \\  & & & & & & & & \\  & & & & & & & & \\ \hline \multirow{4}{*}{**mIOU**} & DCGAN [20] & 66.44 & 38.09 & 30.64 & 18.2 \\  & & SegAdv [29] & 67.79 & 38.33 & 32.11 & 17.4 \\  & & AdvEnt [27] & 73.31 & 52.11 & 42.87 & 17.9 \\  & & DCGAN + Our Preprocessing (M1) & 67.95 & 39.10 & 30.72 & 16.7 \\  & AdvEnt + Our Preprocessing (M2) & 73.57 & 51.25 & 41.61 & 16.5 \\  & & Ours & **75.59** & **57.86** & **44.42** & 16.1 \\ \hline \multirow{4}{*}{**mIOU**} & DCGAN [20] & 86.70 & 62.31 & 56.32 & 18.2 \\  & & SegAdv [29] & **87.18** & 62.25 & 57.17 & 17.4 \\ \cline{1-1}  & & AdvEnt [27] & 86.39 & 63.02 & 57.53 & 17.9 \\ \cline{1-1}  & & Ours & 86.56 & **63.12** & **57.86** & 16.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results on MICC-SRI dataset. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Method**} & **Pixel** & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**F1**} & \multirow{2}{*}{**mIOU**} & \multirow{2}{*}{**FPS**} \\  & & & & & & & \\  & & & & & & & \\ \hline \multirow{4}{*}{**mIOU**} & DCGAN [20] & 66.44 & 38.09 & 30.64 & 18.2 \\  & & SegAdv [29] & 67.79 & 38.33 & 32.11 & 17.4 \\  & & AdvEnt [27] & 73.31 & 52.11 & 42.87 & 17.9 \\  & & DCGAN + Our Preprocessing (M1) & 67.95 & 39.10 & 30.72 & 16.7 \\  & AdvEnt + Our Preprocessing (M2) & 73.57 & 51.25 & 41.61 & 16.5 \\  & & Ours & **75.59** & **57.86** & **44.42** & 16.1 \\ \hline \multirow{4}{*}{**mIOU**} & DCGAN [20] & 86.70 & 62.31 & 56.32 & 18.2 \\  & & SegAdv [29] & **87.18** & 62.25 & 57.17 & 17.4 \\ \cline{1-1}  & & AdvEnt [27] & 86.39 & 63.02 & 57.53 & 17.9 \\ \cline{1-1}  & & Ours & 86.56 & **63.12** & **57.86** & 16.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Semantic segmentation restoration performance mIOU of unsupervised learning on MICC-SRI (%). second pictures in Figure 7, the structural information for the buildings and road can be restored naturally. Figure 5: Unsupervised learning-based Media Integration and Communication Center Semantic Road. Inpainting (MICC-SRI) qualitative results: (**a**) input map, (**b**) ground truth, (**c**) deep convolutional generative adversarial network (DCGAN) [20], (**d**) segmentation adversarial network (SegAdv) [29], (**e**) adversarial entropy network (AdvEnt) [27], and (**f**) semantic segmentation inpainting network (SSIN). Figure 6: Additional results for the unsupervised learning-based Media Integration and Communication Center Semantic Road Inpainting (MICC-SRI) qualitative results: (**a**) input map, (**b**) ground truth, (**c**) deep convolutional generative adversarial network (DCGAN) [20], (**d**) segmentation adversarial network (SegAdv) [29], (**e**) adversarial entropy network (AdvEnt) [27], and (**f**) semantic segmentation inpainting network (SSIN). We also trained the network using the supervised method by adding the ground-truth labels from the MICC-SRI dataset to remove the dynamic objects. From the reconstruction loss function (8), the cross-entropy with the opposite mask (0 for the static objects and 1 for the dynamic objects) is added with a weighting factor of 3, and the adversarial loss weighting factor reduces to 0.001 to focus more on supervised learning. The quantitative evaluation in supervised learning is detailed in Table 3. The results indicate that supervised training does not demonstrate a substantial difference in the results. Because the preprocessing method of the SegAdv was set up on the assumption of supervised learning, the SegAdv [29] network learns to fill in the segmentation map better than the other methods, as in Table 3. However, our method provides slightly better results for the F1 and mIOU scores, meaning that our method recovers detailed information. Regarding supervised learning, the ground truth plays a key role during training.