# 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** & \(mAP_{50}\) & \(mAP_{75}\) & \(mAP_{s}\) & \(mAP_{m}\) & \(mAP_{l}\) \\ \hline \multirow{8}{*}{Faster R-CNN} & Source & 6.6 & 18.8 & 3.5 & 0.8 & 7.9 & 10.1 \\  & FDA & 6.4 & 19.1 & 3.3 & 1.0 & 7.8 & 9.4 \\  & HM & 7.5 & 21.9 & 3.8 & 1.6 & 8.4 & 10.0 \\  & PDA & 7.3 & 21.7 & 3.7 & 1.3 & 8.3 & 10.6 \\  & FDA+HM+PDA & 6.9 & 20.3 & 3.4 & 1.4 & 8.3 & 9.1 \\  & MT & **14.8** & **40.3** & **8.6** & **8.2** & **18.3** & **14.6** \\ \hline \multirow{8}{*}{FCOS} & Source & 7.2 & 23.6 & 2.5 & 1.9 & 8.7 & 9.4 \\  & FDA & 7.8 & 24.8 & 3.0 & 2.5 & 9.4 & 9.8 \\ \cline{1-1}  & HM & 8.7 & 26.1 & 3.8 & 2.7 & 9.8 & 10.6 \\ \cline{1-1}  & PDA & 8.3 & 25.0 & 3.5 & 2.5 & 9.4 & 10.2 \\ \cline{1-1}  & FDA+HM+PDA & 8.2 & 25.5 & 2.8 & 2.5 & 9.1 & 10.6 \\ \cline{1-1}  & MT & **12.2** & **32.6** & **6.8** & **5.7** & **15.2** & **11.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: 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-ZERO to LEVIR [21] 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** & \(mAP_{50}\) & \(mAP_{75}\) & \(mAP_{s}\) & \(mAP_{m}\) & \(mAP_{l}\) \\ \hline \multirow{8}{*}{Faster R-CNN} & Source & 6.0 & 17.3 & 3.1 & 0.5 & 7.3 & 9.3 \\  & FDA & 6.6 & 19.5 & 3.2 & 1.8 & 7.6 & 9.0 \\  & HM & 7.0 & 20.2 & 3.8 & 0.9 & 8.1 & 9.4 \\  & PDA & 7.2 & 22.2 & 3.3 & 1.4 & 8.7 & 9.7 \\  & FDA+HM+PDA & 7.3 & 20.6 & 4.1 & 1.4 & 8.4 & 10.0 \\  & MT & **15.8** & **42.5** & **10.2** & **9.1** & **19.9** & **15.2** \\ \hline \multirow{8}{*}{FCOS} & Source & 8.9 & 26.5 & 3.6 & 3.3 & 10.4 & 10.0 \\  & FDA & 9.2 & **27.2** & 3.5 & 3.7 & 10.0 & 10.6 \\ \cline{1-1}  & HM & 8.6 & 25.9 & 3.2 & 3.0 & 9.8 & 10.7 \\ \cline{1-1}  & PDA & 8.7 & 24.8 & 4.0 & 2.2 & 9.7 & 10.5 \\ \cline{1-1}  & FDA+HM+PDA & 8.8 & 25.2 & 4.4 & 3.4 & 9.9 & 10.5 \\ \cline{1-1}  & MT & **9.5** & 25.5 & **5.0** & **3.8** & **10.8** & **12.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: 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-RANDOM to LEVIR [21] 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** & \(mAP_{50}\) & \(mAP_{75}\) & \(mAP_{s}\) & \(mAP_{m}\) & \(mAP_{l}\) \\ \hline \multirow{8}{*}{Faster R-CNN} & Source & 17.1 & 30.1 & 17.6 & 2.0 & 29.4 & 47.6 \\  & FDA & 15.2 & 28.0 & 15.5 & 2.4 & 26.2 & 40.6 \\ \cline{1-1}  & HM & 17.4 & 31.5 & **18.4** & 2.5 & 30.3 & 47.0 \\ \cline{1-1}  & PDA & 15.5 & 27.5 & 16.8 & 1.8 & 27.2 & 44.4 \\ \cline{1-1}  & FDA+HM+PDA & **17.6** & 32.5 & 17.9 & 2.7 & **30.4** & 45.9 \\ \cline{1-1}  & MT & 16.7 & **33.0** & 15.7 & **3.1** & 25.5 & **50.9** \\ \hline \multirow{8}{*}{FCOS} & Source & 16.6 & 30.1 & 17.3 & 1.8 & 28.7 & 47.3 \\ \cline{1-1}  & FDA & 17.5 & 34.9 & 16.0 & 3.6 & 29.0 & 43.1 \\ \cline{1-1}  & HM & 18.4 & 34.3 & 18.5 & 3.4 & 31.5 & 45.2 \\ \cline{1-1}  & PDA & 18.0 & 33.3 & 18.4 & 3.2 & 30.7 & 46.6 \\ \cline{1-1}  & FDA+HM+PDA & 19.2 & **36.7** & 18.4 & **3.7** & 32.3 & 47.5 \\ \cline{1-1}  & MT & **20.6** & 35.1 & **22.2** & 2.4 & **36.0** & **59.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The Mean Average Precision (mAP, in %) for cross-domain object detection on the validation set of our dataset, i.e., those adapted from SASI-UNITY to airbus-ship-detection [26]. The best mAPs are highlighted in bold.