# DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images
## 4 Experiment and Discussion
### Comparison with the State-of-the-Art Methods  
We compared the proposed DGANet with the state-of-the-art algorithms on two datasets: UCAS-AOD and LEVIR. Experimental results demonstrate that the performance of DGANet exceeds all other published methods. Results on UCAS-AOD. We performed extensive comparison between the proposed detector with other state-of-the-art methods used on the UCAS-AOD dataset. Among the comparison experiments, we chose some well-known frameworks, such as Faster RCNN, YOLOv2, SSD, RFCN, and YOLOv4. In addition, many deep CNNs such as VGG-Net, ResNet50, ResNet101, GooLeNet, and DenseNet65 were involved. The overall comparison performance is listed in Table 5.  
Figure 7: Precision-recall curves of different backbone models used on the UCAS-AOD dataset. In this section, we mainly compare the average precision among multiple frameworks and baselines. From the table, it can be noticed that our method achieved 0.956 for the average precision of the car and 0.987 for the average precision of the plane, and both results are highly competitive with the existing published methods. The mAP of our proposed method reached 0.971. From the results, we note that the precision of our approach is considerably better than that of other methods. Figure 8 shows typical detection results on UCAS-AOD dataset by the proposed method. It can be seen that DGANet can detect objects accurately in a variety of challenging backgrounds, and it performs well in a densely arranged scene. Results on LEVIR. We compare DGANet predicted results with other models on the LEVIR dataset. LEVIR is a new remote sensing images dataset, which has three categories and is used to realize horizontal bounding box target detection. We also compare our network with other competitive methods, such as Faster RCNN, Sig-NMS, SSD300, RetinaNet500, and Centernet. Table 6 shows the comparison results among different models on the LEVIR dataset. From the data in Table 6 it is apparent that DGANet achievods 0.863 for the mAP, which pulls away from other existing published methods. Compared with the well-known methods Faster RCNN and SSD, DGANet outperformed them with a 6.1% and 7% promotion on mAP. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Term**} & \multicolumn{4}{c}{**AP**} \\ \cline{2-5}  & **Plane** & **Ship** & **Oil-Tank** & **mAP** \\ \hline MEDIUM-RAM [49] & 0.760 & 0.500 & 0.481 & 0.580 \\ LARGE-RAM [49] & 0.717 & 0.608 & 0.430 & 0.585 \\ Soft-NMS [57] & 0.812 & 0.791 & 0.612 & 0.738 \\ Sig-NMS [57] & 0.868 & 0.794 & 0.682 & 0.781 \\ SSD300 [14] & 0.877 & 0.815 & 0.687 & 0.793 \\ Fater R-CNN [27] & 0.876 & 0.816 & 0.719 & 0.804 \\ RetinaNet500 [32] & 0.876 & 0.802 & 0.740 & 0.806 \\ CenterNet [36] & 0.867 & 0.829 & 0.788 & 0.828 \\ DGANet (ours) & 0.886 & 0.872 & 0.832 & 0.863 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation results on the LEVIR dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Term**} & \multirow{2}{*}{**Backbone**} & \multicolumn{3}{c}{**AP**} \\ \cline{3-5}  & & **Car** & **Plane** & **mAP** \\ \hline YOLOv2 [44] & VGG16 & 0.669 & 0.855 & 0.762 \\ SSD [44] & VGG16 & 0.813 & 0.891 & 0.852 \\ RFCN [44] & ResNet101 & 0.806 & 0.899 & 0.853 \\ R-DFPN [51] & ResNet101 & 0.825 & 0.959 & 0.892 \\ Faster RCNN [44] & VGG16 & 0.879 & 0.907 & 0.893 \\ YOLOv2-A [5] & GoogLeNet & 0.882 & 0.907 & 0.894 \\ YOLOv3 [29] & DarkNet53 & 0.850 & 0.972 & 0.911 \\ YOLOv4 [52] & DarkNet53 & 0.926 & 0.958 & 0.942 \\ S\({}^{2}\)ARN [53] & ResNet50 & 0.922 & 0.976 & 0.949 \\ RetinaNet-H [54] & ResNet50 & 0.936 & 0.973 & 0.955 \\ MS-Densenet [55] & DenseNet65 & & & 0.957 \\ FADet [56] & ResNet101 & 0.927 & 0.987 & 0.957 \\ R\({}^{3}\)Det [54] & ResNet152 & 0.941 & 0.982 & 0.962 \\ DGANet(ours) & DLA34 & 0.956 & 0.987 & 0.971 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation results for the UCAS-AOD dataset.