## 4 Experiments
### Visualizing Dynamic Anchor  
We visualized the predicted anchors and the ground-truth boxes on the input images. As shown in Figure 7, our method successfully produces multiple anchor boxes for objects, and the predicted anchors have the highest coverages with the corresponding ground-truth boxes. As shown in Figure 7a, for an image containing dense objects, the pre-defined anchor strategy generally improves the recall rate by setting anchor boxes intensively to prevent missing targets. The method proposed in this paper introduces the a priori information of object shape, constructs the corresponding anchor box according to the features of the object, and obtains a large number of candidate boxes with high IoU scores. It can not only maintain a high recall rate, but also reduce the number of anchors on a large scale; In particular, because the size and shape of the pre-defined anchors are fixed, it is unable to handle the objects with too small a size or extreme aspect ratio well, and hyper-parameters needed to be adjusted in the experiment. Our method predicts the appropriate anchors based on the semantic characteristics of each object, which perfectly avoids the above problems and has stronger applicability. As shown in Figure 7b, dynamic anchor generates a large number of anchor boxes for small-scale targets, and the offset distances between these anchor boxes and the ground-truth boxes are very small. These anchor boxes are very suitable for regression. As shown in Figure 7c, the predicted anchor boxes have shapes similar to the ground-truth boxes, and the aspect ratio is very large. To evaluate the quality of all predicted anchors, we treated the predicted anchors as the prediction results. The detection performance reached a considerable 26.5 AP, proving the effectiveness of dynamic anchor.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **AP** & **AP\({}_{\text{50}}\)** & **AP\({}_{\text{75}}\)** & **AR\({}_{\text{100}}\)** & **AR\({}_{\text{300}}\)** & **AR\({}_{\text{1000}}\)** \\ \hline None & 36.7 & 54.3 & 39.8 & 52.9 & 52.9 & 52.9 \\ Quality & **37.2** & **54.6** & **40.2** & **54.7** & **54.7** & **54.7** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study for quality branch on the MS COCO minival set. “None” denote that no “quality” is used. “Quality” is that using quality predicted from the quality branch. Bold font indicates the best results.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sampling Ratio** & **AP** & **AP\({}_{\text{50}}\)** & **AP\({}_{\text{75}}\)** & **AP\({}_{\text{S}}\)** & **AP\({}_{\text{M}}\)** & **AP\({}_{\text{L}}\)** & **AR\({}_{\text{S}}\)** & **AR\({}_{\text{M}}\)** & **AR\({}_{\text{L}}\)** \\ \hline None & 37.2 & 54.4 & 40.2 & 20.2 & 41.6 & 50.4 & **33.5** & 59.0 & 70.6 \\
1 & 37.2 & 54.6 & 40.2 & **20.5** & 42.3 & 49.5 & 32.6 & **59.9** & **71.4** \\
1.5 & 37.3 & 54.8 & 40.2 & 20.1 & 42.1 & 49.6 & 33.2 & 59.6 & 70.6 \\
2 & **37.5** & **54.9** & **40.5** & 20.1 & **42.4** & **50.7** & 32.5 & 59.3 & 70.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of different center sampling ratio on the MS COCO minival set. Bold font indicates the best results.  
Figure 7: Visualization results of predicted anchors (green boxes) and ground-truth boxes (red boxes) in DA-RetinaNet. (**a**) Input images with dense objects; (**b**) input images with small-scale objects; (**c**) input images with extremely wide objects.