# Syn2 Real Detection in the Sky: Generation and Adaptation of Synthetic Aerial Ship Images
## 4 Experiments
### Observation and Analysis  
In the experiments conducted with the TGRS-HRRSD-Dataset [9] as the target data, it was observed that (apart from the \(mAP_{s}\) values of the experimental comparison group where UNITY was used as the source data and FCOS as the model, as well as the experimental comparison group where RANDOM was used as the source data and Faster R-CNN as the model) the mAP values of each group were the highest for the MT method. Similarly, in the experiments using LEVIR [21] as the target data (except for the \(mAP_{l}\) values of the experimental comparison group with UNITY as the source data and FCOS as the model, as well as the \(mAP_{50}\) values of the experimental comparison group with RANDOM as the source data and FCOS as the model), the mAP values of each group were again the highest for the MT method. However, in the experiments where airbus-ship-detection [26] was used as the target data, no clear pattern was observed in the experimental results, and most of the highest mAP values could be attributed to the use of domain-adaptive augmentation methods. Taken together, we can make the following observations. (1) In the comparison between Faster R-CNN and FCOS, the Faster R-CNN model produced better results. This is because two-stage algorithms involve an additional step of generating candidate regions, which one-stage algorithms do not have. In tasks where real-time performance is not required, two-stage algorithms generally perform better. \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 & 17.6 & 32.5 & 16.8 & 1.7 & 28.4 & 52.3 \\  & FDA & 17.3 & 35.7 & 14.5 & 2.7 & 27.8 & 48.1 \\  & HM & **19.1** & **38.3** & **17.3** & **3.0** & **31.3** & 50.9 \\  & PDA & 18.1 & 37.3 & 15.6 & 2.4 & 29.6 & 50.4 \\  & FDA+HM+PDA & 18.4 & 37.7 & 16.1 & 2.8 & 29.8 & 49.8 \\  & MT & 16.3 & 33.2 & 14.4 & 1.4 & 22.4 & **61.9** \\ \hline \multirow{8}{*}{FCOS} & Source & 18.8 & 37.0 & 17.0 & 3.0 & 31.6 & 48.6 \\  & FDA & 18.0 & 37.4 & 14.7 & 3.2 & 29.2 & 48.3 \\ \cline{1-1}  & HM & **19.4** & 39.3 & 17.2 & **3.4** & **32.9** & 49.9 \\ \cline{1-1}  & PDA & 19.1 & 39.4 & 16.7 & 3.3 & 31.5 & 50.0 \\ \cline{1-1}  & FDA+HM+PDA & **19.4** & **39.7** & 16.8 & 3.2 & 31.9 & 50.6 \\ \cline{1-1}  & MT & **19.4** & 37.8 & **18.7** & 2.3 & 32.3 & **57.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The Mean Average Precision (mAP, in %) for cross-domain object detection on the validation set of our dataset, i.e., from those adapted SASI-RANDOM to airbus-ship-detection [26]. 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 & 17.2 & 32.7 & 17.5 & 1.9 & 27.6 & 51.7 \\  & FDA & 17.7 & 35.7 & 16.0 & 2.2 & 28.8 & 50.4 \\  & HM & **19.9** & **38.4** & **19.0** & **2.6** & **32.3** & 54.7 \\  & PDA & 18.6 & 37.2 & 16.9 & 2.3 & 30.2 & 52.9 \\  & FDA+HM+PDA & 18.7 & 36.2 & 18.1 & 2.3 & 30.3 & 53.9 \\  & MT & 13.7 & 30.4 & 10.7 & 1.2 & 18.2 & **57.0** \\ \hline \multirow{8}{*}{FCOS} & Source & 18.8 & 36.8 & 17.9 & 2.3 & 31.5 & 52.2 \\  & FDA & 18.7 & 36.9 & 17.4 & 2.8 & 31.3 & 51.0 \\ \cline{1-1}  & HM & **20.5** & **40.9** & **18.7** & **3.3** & **33.9** & 53.8 \\ \cline{1-1}  & PDA & 19.7 & 39.0 & 18.6 & 2.7 & 32.8 & 53.7 \\ \cline{1-1}  & FDA+HM+PDA & 19.7 & 38.9 & 18.2 & **3.3** & 33.5 & 52.9 \\ \cline{1-1}  & MT & 17.6 & 35.4 & 16.5 & 1.9 & 28.9 & **57.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The Mean Average precision (mAP, in %) for cross-domain object detection on the validation set of our dataset, i.e., those adapted from SASI-ZERO to airbus-ship-detection [26]. The best mAPs are highlighted in bold.