# Integrating Prior Knowledge into Attention for Ship Detection in SAR Images
## 5 Experiments and Results
### Qualitative Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Algorithm** & \(AP\) & \(AP_{50}\) & \(AP_{75}\) & \(AP_{S}\) & \(AP_{M}\) & \(AP_{L}\) \\ \hline Faster R-CNN & 0.659 & 0.959 & 0.776 & 0.638 & 0.700 & 0.678 \\ + BDAM & **0.664** & **0.961** & **0.798** & **0.643** & **0.702** & **0.720** \\ + CBAM & 0.655 & 0.959 & 0.781 & 0.634 & 0.693 & 0.681 \\ + EAM & 0.657 & 0.961 & 0.776 & 0.635 & 0.692 & 0.678 \\ + BCA & 0.660 & 0.961 & 0.762 & 0.639 & 0.701 & 0.663 \\ \hline RetinaNet & 0.639 & 0.921 & 0.750 & 0.616 & 0.687 & 0.601 \\ + BDAM & **0.641** & **0.942** & 0.762 & **0.621** & 0.681 & **0.678** \\ + CBAM & 0.637 & 0.925 & **0.764** & 0.612 & 0.689 & 0.620 \\ + EAM & 0.634 & 0.927 & 0.750 & 0.605 & 0.696 & 0.635 \\ + BCA & 0.641 & 0.931 & 0.758 & 0.612 & **0.702** & 0.656 \\ \hline SSD & 0.548 & 0.885 & 0.643 & 0.515 & 0.610 & 0.596 \\ + BDAM & **0.555** & 0.891 & **0.651** & **0.520** & **0.626** & **0.598** \\ + CBAM & 0.544 & 0.885 & 0.645 & 0.511 & 0.623 & 0.558 \\ + EAM & 0.551 & **0.897** & 0.631 & 0.520 & 0.615 & 0.545 \\ + BCA & 0.499 & 0.834 & 0.558 & 0.450 & 0.585 & 0.548 \\ \hline YOLOv4 & 0.499 & 0.937 & 0.472 & 0.453 & 0.633 & 0.637 \\ + BDAM & **0.508** & 0.942 & 0.464 & 0.452 & **0.647** & **0.729** \\ + CBAM & 0.507 & 0.958 & 0.464 & **0.457** & 0.639 & 0.657 \\ + EAM & 0.504 & 0.940 & **0.485** & 0.452 & 0.644 & 0.675 \\ + BCA & 0.505 & **0.952** & 0.452 & 0.454 & 0.632 & 0.713 \\ \hline \end{tabular}
\end{table} 
Table 6: Experimental results of different algorithms on dataset SSDD. Bolded data represent the best and underlined data represent the second best. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Algorithm** & \(AP\) & \(AP_{50}\) & \(AP_{75}\) & \(AP_{S}\) & \(AP_{M}\) & \(AP_{L}\) \\ \hline Faster R-CNN & **0.626** & 0.866 & 0.716 & 0.502 & **0.768** & 0.599 \\ + BDAM & 0.625 & **0.876** & **0.719** & **0.505** & 0.764 & **0.628** \\ + CBAM & 0.625 & 0.875 & 0.712 & 0.505 & 0.761 & 0.612 \\ + EAM & 0.621 & 0.866 & 0.710 & 0.500 & 0.763 & 0.615 \\ + BCA & 0.613 & 0.855 & 0.707 & 0.493 & 0.749 & 0.618 \\ \hline RetinaNet & 0.561 & 0.787 & 0.627 & 0.380 & 0.765 & 0.507 \\ + BDAM & **0.567** & **0.796** & **0.633** & **0.387** & 0.764 & **0.566** \\ + CBAM & 0.561 & 0.785 & 0.627 & 0.380 & **0.765** & 0.494 \\ + EAM & 0.558 & 0.784 & 0.625 & 0.377 & 0.763 & 0.546 \\ + BCA & 0.554 & 0.779 & 0.615 & 0.382 & 0.753 & 0.475 \\ \hline SSD & 0.449 & 0.686 & 0.510 & 0.255 & 0.674 & 0.537 \\ + BDAM & **0.457** & **0.694** & **0.518** & 0.257 & **0.684** & **0.539** \\ + CBAM & 0.454 & 0.690 & 0.513 & **0.259** & 0.683 & 0.528 \\ + EAM & 0.453 & 0.691 & 0.506 & 0.250 & 0.683 & 0.538 \\ + BCA & 0.437 & 0.666 & 0.498 & 0.246 & 0.662 & 0.421 \\ \hline YOLOv4 & 0.550 & 0.914 & 0.603 & **0.395** & 0.686 & 0.607 \\ + BDAM & **0.552** & **0.920** & 0.618 & 0.389 & **0.702** & **0.650** \\ + CBAM & 0.550 & 0.912 & 0.610 & 0.377 & 0.690 & 0.621 \\ + EAM & 0.540 & 0.920 & 0.578 & 0.376 & 0.680 & 0.619 \\ + BCA & 0.545 & 0.912 & 0.600 & 0.382 & 0.690 & 0.616 \\ \hline \end{tabular}
\end{table} 
Table 7: Experimental results of different algorithms on dataset HRSID. Bolded data represent the best and underlined data represent the second best. Figure 10: Ground truth and recognition results of some images. (**a**) Ground truth. (**b**) Faster R-CNN. (**c**) Faster R-CNN with BDAM. (**d**) YOLOv4. (**e**) YOLOv4 with BDAM.