# Syn2 Real Detection in the Sky: Generation and Adaptation of Synthetic Aerial Ship Images
## 4 Experiments
### DAOD Experiments
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Model** & **Method** & **mAP** & \(mAP_{\textbf{30}}\) & \(mAP_{\textbf{75}}\) & \(mAP_{\textbf{8}}\) & \(mAP_{m}\) & \(mAP_{l}\) \\ \hline \multirow{4}{*}{Faster R-CNN} & Source & 33.3 & 56.9 & 35.1 & **2.0** & 19.2 & 45.9 \\  & FDA & 32.9 & 58.6 & 34.5 & 1.6 & 19.8 & 44.3 \\  & HM & 27.9 & 55.9 & 23.8 & 0.6 & 15.8 & 39.4 \\  & PDA & 35.9 & 64.7 & 36.3 & 1.0 & 22.9 & 47.8 \\  & FDA+HM+PDA & 30.3 & 57.6 & 27.9 & 0.3 & 16.7 & 42.6 \\  & MT & **52.9** & **81.0** & **63.8** & 1.9 & **45.0** & **61.5** \\ \hline \multirow{4}{*}{FCOS} & Source & 32.6 & 62.0 & 31.1 & 2.9 & 23.6 & 41.2 \\  & FDA & 33.2 & 62.3 & 32.9 & 1.9 & 20.7 & 44.1 \\  & HM & 30.4 & 59.1 & 27.2 & 0.3 & 17.5 & 41.8 \\  & PDA & 36.0 & 66.3 & 35.4 & 1.6 & 25.5 & 46.5 \\  & FDA+HM+PDA & 32.3 & 62.5 & 31.4 & 0.8 & 20.2 & 43.4 \\  & MT & **47.4** & **81.6** & **49.7** & **4.0** & **38.2** & **59.9** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The Mean Average Precision (mAP, in %) of different models and methods for the cross-domain object detection on the validation set of our dataset, i.e., for SASI-RANDOM to TGRS-HRRSD-Dataset [9] adaptation. The best mAPs are highlighted in bold. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Model** & **Method** & **mAP** & \(mAP_{\textbf{30}}\) & \(mAP_{\textbf{75}}\) & \(mAP_{\textbf{8}}\) & \(mAP_{m}\) & \(mAP_{l}\) \\ \hline \multirow{4}{*}{Faster R-CNN} & Source & 33.1 & 57.7 & 34.6 & 2.1 & 18.9 & 45.4 \\  & FDA & 33.1 & 58.8 & 34.0 & 2.1 & 21.1 & 43.8 \\  & HM & 27.0 & 52.5 & 25.5 & 0.3 & 16.5 & 37.4 \\  & PDA & 36.0 & 62.6 & 37.4 & 0.9 & 23.0 & 47.7 \\  & FDA+HM+PDA & 28.0 & 51.7 & 26.7 & 0.2 & 15.2 & 39.4 \\  & MT & **44.2** & **80.7** & **43.0** & **4.5** & **40.5** & **50.3** \\ \hline \multirow{4}{*}{FCOS} & Source & 34.6 & 62.0 & 34.3 & 0.8 & 22.1 & 45.6 \\  & FDA & 31.7 & 59.1 & 30.9 & 0.9 & 19.2 & 42.7 \\ \cline{1-1}  & HM & 29.7 & 59.1 & 26.6 & 0.1 & 18.2 & 41.1 \\ \cline{1-1}  & PDA & 34.9 & 64.2 & 33.5 & 0.2 & 21.6 & 47.0 \\ \cline{1-1}  & FDA+HM+PDA & 31.3 & 60.5 & 29.1 & 0.3 & 19.0 & 42.8 \\ \cline{1-1}  & MT & **53.6** & **86.8** & **61.2** & **2.1** & **45.3** & **62.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The Mean Average Precision (mAP, in %) of different models and methods for the cross-domain object detection on the validation set of our dataset, i.e., SASI-ZERO to TGRS-HRRSD-Dataset [9] adaptation. The best mAPs are highlighted in bold. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Model** & **Method** & **mAP** & \(mAP_{\textbf{30}}\) & \(mAP_{\textbf{75}}\) & \(mAP_{\textbf{8}}\) & \(mAP_{m}\) & \(mAP_{l}\) \\ \hline \multirow{4}{*}{Faster R-CNN} & Source & 8.0 & 21.2 & 4.9 & 0.9 & 9.1 & 11.6 \\  & FDA & 8.2 & 21.1 & 5.2 & 1.7 & 8.6 & 11.1 \\  & HM & 8.4 & 22.2 & 4.7 & 1.5 & 9.3 & 10.9 \\  & PDA & 8.4 & 22.6 & 5.3 & 1.2 & 9.3 & 11.5 \\  & FDA+HM+PDA & 7.7 & 20.6 & 4.5 & 1.3 & 8.5 & 11.0 \\  & MT & **12.3** & **32.9** & **8.4** & **8.4** & **13.1** & **14.4** \\ \hline \multirow{4}{*}{FCOS} & Source & 9.2 & 23.6 & 5.5 & 1.4 & 11.2 & 10.6 \\  & FDA & 9.2 & 24.2 & 5.3 & 2.9 & 11.0 & 11.1 \\ \cline{1-1}  & HM & 9.0 & 23.2 & 4.8 & 2.1 & 11.0 & 10.8 \\ \cline{1-1}  & PDA & 8.8 & 22.9 & 4.7 & 2.1 & 10.0 & 10.9 \\ \cline{1-1}  & FDA+HM+PDA & 9.7 & 24.3 & 5.7 & 2.4 & 11.5 & **11.5** \\ \cline{1-1}  & MT & **11.7** & **30.6** & **6.9** & **6.0** & **14.9** & 10.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The Mean Average Precision (mAP) in percentage for various models and methods for the cross-domain object detection on the validation set of our dataset. The results are presented for the adaptation of SASI-UNITY to LEVIR [21] adaptation. The best mAPs are highlighted in bold.