# DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images
## 4 Experiment and Discussion
### Ablation Experiments
#### 4.2.1 Evaluation of Dynamic Gradient Adjustment  
In order to verify the superiority of DGA module, we conducted multiple sets of experiments to compare the performance among the original CenterNet and different DGA frameworks. Table 1 shows some of the main contrast results of different classification modules. First, we introduced an equilibrium coefficient \(\gamma\in[0,1]\) to address the imbalance between positive and negative examples. The parameter \(\gamma\) was introduced to avoid overlearning easy negative examples that contribute little to the detector, which was set to 0.25 after a series of experiments. A comparison of the results between ORIG and DGA-1 revealed that mAP increased from 0.942 to 0.961 with an increase of 1.9 percent. Second, gradient density was used to dynamically refine the Focal Loss function to harmonize the imbalance easy and hard examples. DGA-2 stands for the updated gradient harmonizing mechanism, which introduces two hyper-parameters \(\alpha\) and \(\beta\) to improve the recognition ability of the model to real samples. We have designed verification experiments and obtained positive results. When hyper-parameters were equal to 0, the performance of the detector degraded, and the prediction accuracy of GDA-3 deteriorated to 0.927. DGA-4 defines \(\beta\) as equal to 4, and the mAP reached 0.952, which achieves 2.5% higher than DGA-3 and 1% higher than ORIG. DGA-5 set the value of parameter \(\alpha\) to 2, and the accuracy of the model reached 0.956. Finally, the dynamic gradient adjustment algorithm contains all three optimized parameters \((\gamma,\alpha,\beta)\), and the framework is abbreviated with DGA. The DGA framework showed the best result with mAP of 0.966 and mRecall of 0.987 among all models in the contrast experiments. Especially for small and dense vehicle objects, the accuracy increased by 2.4% compared with the Focal Loss scheme, and the mean recall increased by 1.9%. The precision-recall curve is an important indicator of the performance of the CNN network. The large the area between the P-R curve and coordinate axeses, the better the class prediction performance of the mode. In Figure 5, we have plotted P-R curves  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Term**} & \multirow{2}{*}{\(\gamma\)} & \multirow{2}{*}{\(\alpha\)} & \multirow{2}{*}{\(\beta\)} & \multirow{2}{*}{**GD (g)**} & \multicolumn{3}{c}{**AP**} & \multicolumn{3}{c}{**Recall**} & \multirow{2}{*}{**mAP**} & \multirow{2}{*}{**mRecall**} \\ \cline{5-5} \cline{8-10}  & & & & & **Car** & & & **Plane** & & \\ \hline ORIG & \(\backslash\) & 2 & 4 & \(\backslash\) & 0.922 & 0.962 & 0.959 & 0.978 & 0.942 & 0.968 \\ DGA-1 & 0.25 & 2 & 4 & \(\backslash\) & 0.946 & 0.976 & 0.976 & 0.992 & 0.961 & 0.984 \\ DGA-2 & \(\backslash\) & 2 & 4 & \(\surd\)  
of different classification networks on the UCAS-AOD dataset. By comparing the two pictures, it can be found that the AP of the car dataset is lower. This is because the size of a car is less than that of plane. The precision and recall were significantly improved by introducing the DGA module. Our improvement scheme resulted in an improvement of 3.2% and 1.6% on AP for the car and plane. The results, as shown in Figure 5, indicate that our proposed DGA classification module obviously achieves the best object prediction performance. Some qualitative results between the original network and our proposed DGA module are illustrated in Figure 6. Both models can realize the detection of car and plane objects in complex backgrounds. However, there are some differences between these two networks. On the one hand, the prediction confidence of the propose model for significant objects is higher. On the other hand, there were more mispredictions and omissions for the original network. Some car objects in the shadow could not be predicted accurately. Compared with the original network, the proposed method could better overcome the interference of similar objects in the background.