## 4 Experiments and Results
### Datasets  
The RSOD datasets are open object detection datasets for object detection in remote sensing images. The datasets include aircraft, fuel tanks, sports fields, and overpasses that have been annotated in the format of PASCAL VOC [40] datasets. The datasets are divided into four folders as follow:  
1. 4993 aircraft in 446 images;
2. 191 playgrounds in 189 images;
3. 180 overpasses in 176 images;
4. 1586 oil tanks in 165 images.  
Some example images from the RSOD datasets are shown in Figure 8.  
We randomly divided the datasets into a training set, a validation set, and a test set according to a 6:2:2 ratio, i.e., 580 images for training, 197 images for validation, and 199 images for testing, as shown in Table 2.  
Figure 8: Images in the RSOD datasets. (**a**) Playground; (**b**) overpass; (**c**) oil tank; and (**d**) aircraft.  
Figure 7: K-means algorithm is used to generate matching anchors for objects of different sizes.  
The DIOR datasets are large-scale benchmark datasets for object detection in optical remote sensing images. The datasets includes 23,463 images of different seasons and weather patterns, with 190,288 object instances, a uniform image size of 800 \(\times\) 800, and resolutions ranging from 0.5 m to 30 m. The DIOR datasets officially provided helped us divide the training set, verification set, and test set according to the ratio of 2.5:2.5:5 as shown in Table 3[39]. Note that one image may contain multiple object classes, so the column totals do not simply equal the sums of each corresponding column. The number of each category represents the object number, not the number of images, and the \"Total\" in last line represents the number of images in each set.  
Some example images from the DIOR datasets are shown in Figure 9.  
As shown in the figure, the scenes in the DIOR datasets and RSOD datasets are relatively complex, including scenes such as mountains, lakes, grasslands, farms, docks, and airports. The scales of the different object categories vary greatly, ranging from small objects such as airplanes and cars, with sizes less than 30 \(\times\) 30, to playgrounds and golf courses, with sizes larger than 500 \(\times\) 500. The scales of similar objects, such as ships and airplanes, also vary greatly.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Class** & **Train** & **Val** & **Test** \\ \hline Aircraft & 268 & 88 & 90 \\ Oil tank & 93 & 36 & 36 \\ Overpass & 106 & 35 & 35 \\ Playground & 113 & 38 & 38 \\ Total & 580 & 197 & 199 \\ \hline \end{tabular}
\end{table}
Table 2: Training, validation, and test sets for each category of the RSOD datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Class** & **Train** & **Val** & **Test** \\ \hline Airplane & 344 & 338 & 705 \\ Airport & 326 & 327 & 657 \\ Baseball field & 551 & 557 & 1312 \\ Basketball court & 336 & 329 & 704 \\ Bright & 379 & 495 & 1302 \\ Chimney & 202 & 204 & 448 \\ Dam & 238 & 246 & 502 \\ Expressway service area & 279 & 281 & 565 \\ Expressway toll station & 285 & 299 & 634 \\ Golf field & 216 & 239 & 491 \\ Ground track field & 536 & 454 & 1322 \\ Harbor & 328 & 332 & 814 \\ Overpass & 410 & 510 & 1099 \\ Ship & 650 & 652 & 1400 \\ Stadium & 289 & 292 & 619 \\ Storage tank & 391 & 384 & 839 \\ Tennis court & 605 & 630 & 1347 \\ Train station & 244 & 549 & 501 \\ Vehicle & 1556 & 1558 & 3306 \\ Windmill & 403 & 404 & 809 \\ Total & 5862 & 5863 & 11,738 \\ \hline \end{tabular}
\end{table}
Table 3: Training, validation and test sets for each category of the DIOR datasets.