# Syn2 Real Detection in the Sky: Generation and Adaptation of Synthetic Aerial Ship Images
## 4 Experiments
### DAOD Experiments  
The synthetic datasets generated in Sections 3.1 and 3.2 serve as the source data, while the real-world datasets serve as the target data. Initially, we performed separate object detection on the synthetic dataset to obtain the results of the \"Source\" method. Subsequently, the results were processed using three style transfer methods, namely \"FDA\", \"HM\", and \"PDA\", to obtain domain-adaptive results for a single-style transfer. For mixed-style transfer, the domain-adaptive results of the \"FDA+HM+PDA\" method were obtained. Finally, employing the mean teacher method, we obtained domain-adaptive results, which are marked as \"MT\". All experimental results were evaluated according to the COCO standard evaluation metrics. The IoU threshold is the measure of overlap between the selected bounding box and the predicted bounding box of the object. The mAP value denotes the average precision across 10 thresholds, ranging from IoU 0.50 to 0.95 in increments of 0.05. The \(mAP_{50}\) value represents the average precision at an IoU threshold of 0.50. The \(mAP_{5}\) value represents the average precision at an IoU threshold of 0.75. The \(mAP_{s}\) indicates the precision of detecting small objects, \(mAP_{m}\) denotes the precision of detecting medium objects, and \(mAP_{s}\) signifies the precision of detecting large objects. In the experiments of this paper, the mAP values were the focus of attention. The experimental results of the cross-domain object detection, obtained from applying our datasets to the TGRS-HRRSD-Dataset [9], are presented in Tables 2-4. The experimental results of the cross-domain object detection, obtained from using our datasets on the LEVIR dataset [21], are shown in Tables 5-7. Additionally, the experimental results of the cross-domain object detection, obtained by employing our datasets on the airbus-ship-detection dataset [26], are displayed in Tables 8-10. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Model** & **Method** & **mAP** & \(mAP_{\text{30}}\) & \(mAP_{\text{75}}\) & \(mAP_{s}\) & \(mAP_{m}\) & \(mAP_{\text{1}}\) \\ \hline \multirow{8}{*}{Faster R-CNN} & Source & 25.6 & 49.8 & 24.2 & 0.6 & 24.6 & 27.0 \\  & FDA & 22.3 & 45.7 & 19.0 & 0.3 & 18.8 & 26.4 \\  & HM & 22.5 & 47.1 & 18.7 & 0.2 & 20.9 & 24.4 \\  & PDA & 27.8 & 55.4 & 25.2 & 2.2 & 24.6 & 31.6 \\  & FDA+HM+PDA & 26.2 & 53.9 & 22.4 & 0.9 & 24.3 & 28.8 \\  & MT & **43.1** & **77.9** & **42.0** & **23.0** & **47.8** & **40.7** \\ \hline \multirow{8}{*}{FCOS} & Source & 22.9 & 47.3 & 20.8 & 3.2 & 23.7 & 23.7 \\  & FDA & 23.8 & 50.5 & 19.7 & 3.4 & 21.9 & 26.9 \\ \cline{1-1}  & HM & 22.5 & 50.0 & 15.8 & 1.1 & 18.7 & 27.9 \\ \cline{1-1}  & PDA & 27.6 & 56.7 & 24.3 & **4.2** & 25.8 & 30.8 \\ \cline{1-1}  & FDA+HM+PDA & 23.9 & 52.6 & 18.2 & 2.0 & 21.7 & 27.6 \\ \cline{1-1}  & MT & **42.6** & **79.3** & **41.4** & 0.7 & **34.5** & **52.9** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: 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-UNITY to TGRS-HRRSD-Dataset [9] adaptation. The best mAPs are highlighted in bold.