## 3 Results and Discussion
### Evaluation with COCO API
As presented in Figures 11-13, precision-recall (PR) curves were plotted for small-, medium-, large-scale objects and for all object sizes across the three models. The evaluations were performed for the DOTA test set and large-scale image set separately. The orange area out of the curves represents the false negative (FN) portion of the evaluated data set. In other words, it is the PR after all errors are removed. The purple area presents the falsely detected objects, which are the backgrounds in the dataset (BG). The blue area presents the localization errors of the predicted boxes (Loc) and indicates that the PR curve is a 0.1 IOU value. The white area shows the area under the precision-recall curve, which is comprised of the prediction with IOU above 0.75 (C75). Lastly, the grey area represents the detections with IOU above 0.5 (C50). The brown area (Sim) is the PR curve after the super-category false positives are removed. Green area (Oth) is the PR after all class confusions are removed. As this research does not include a super-category or any other category, these curves do not exist in the provided plots. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{3}{c}{**DOTA Training Set**} & \multicolumn{3}{c}{**DOTA Test Set**} & \multicolumn{3}{c}{**Large Scale Image Set**} \\ \hline \multirow{3}{*}{} & \multirow{3}{*}{Yolo-v3} & SSD & F RCNN & \multirow{3}{*}{Yolo-v3} & SSD & F RCNN & \multirow{3}{*}{Yolo-v3} & SSD & F RCNN \\  & & (2 nd) & (2 nd) & & & (2 nd) & & & (2 nd) \\ \hline Metric 1 & 0.428 & 0.411 & 0.481 & 0.391 & 0.371 & 0.451 & 0.148 & 0.151 & 0.172 \\ Metric 2 & 0.806 & 0.711 & 0.757 & 0.7  
When the PR plots were investigated together with the AP metrics, which is presented in Table 5, it was observed that the large-sized aircrafts were detected better for the DOTA test and large-scale image set. Additionally, it can be asserted that all of the networks detect better with the IOU above 0.5 when the margin area was compared with IOU above 0.75. The localization error for the DOTA test set is smaller compared with large-scale images. For the Yolo-v3 network, nine optimum anchor sizes were selected by clustering the whole DOTA training samples according to the object sizes; however, the pixel sizes of objects are much smaller in the large-scale image set. Besides, the number of objects in the DOTA training set is much more than the large-scale image set, which possibly resulted in unbalanced object sizes between the two datasets. Lastly, the sizes of the anchor boxes that were selected with the k-means algorithm in the training phase did not match with the optimum size for the large-scale image dataset. This condition could be an explanation for the higher localization errors observed for the large-scale image set. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & \multicolumn{6}{c}{**DOTA Test Set**} & \multicolumn{6}{c}{**Large Scale Image Set**} \\ \hline
**AP** & **C75** & **C50** & **Loc** & **Id** & **C75** & **C50** & **Loc** & **S5** \\ \hline Yolo-v3 All & 0.34 & 0.76 & 0.78 & 0.79 & 0.08 & 0.43 & 0.79 & 0.79 \\ Yolo-v3 Large & 0.36 & 0.80 & 0.82 & 0.83 & 0.02 & 0.22 & 0.69 & 0.69 \\ Yolo-v3 Medium & 0.33 & 0.67 & 0.70 & 0.71 & 0.10 & 0.51 & 0.85 & 0.85 \\ Yolo-v3 Small & 0 & 0.25 & 0.25 & 0.25 & 0.04 & 0.11 & 0.48 & 0.48 \\ SSD All (2 nd) & 0.41 & 0.61 & 0.64 & 0.67 & 0.07 & 0.43 & 0.74 & 0.78 \\ SSD Large (2 nd) & 0.48 & 0.68 & 0.70 & 0.70 & 0.06 & 0.41 & 0.76 & 0.77 \\ SSD Medium (2 nd) & 0.29 & 0.52 & 0.59 & 0.63 & 0.11 & 0.51 & 0.77 & 0.80 \\ SSD Small (2 nd) & 0 & 0.13 & 0.13 & 0.25 & 0 & 0.15 & 0.41 & 0.62 \\ Faster R-CNN All (2 nd) & 0.51 & 0.71 & 0.72 & 0.73 & 0.13 & 0.36 & 0.81 & 0.81 \\ Faster R-CNN Large (2 nd) & 0.56 & 0.77 & 0.78 & 0.79 & 0.22 & 0.52 & 0.82 & 0.82 \\ Faster R-CNN Medium (2 nd) & 0.42 & 0.60 & 0.61 & 0.61 & 0.13 & 0.37 & 0.83 & 0.83 \\ Faster R-CNN Small (2 nd) & 0.06 & 0.17 & 0.17 & 0.25 & 0.04 & 0.14 & 0.55 & 0.59 \\ \hline \hline \end{tabular}
\end{table}