## 4 Experiments
### Ablation Study on COCO  
Feature Enhancement with DCN: To verify the effectiveness of DCN [31] in the feature enhancement module, we designed several comparative experiments. We replaced the DCN with a spatial attention module (SAM) [35]. The SAM converts the shape map into a single-channel spatial attention map. The spatial attention map is multiplied by the corresponding feature map from FPN to obtain the output of the feature enhancement module. To be fair, we also did not use DCN nor direct output the original feature map. As shown in Table 1, the DCN does help the improvement in detection performance. Compared with *, DCN increases the APL and ARL by 1.6% and 0.7%, respectively, and achieves 37.0% AP. Generally, large-scale objects contain richer semantic features. With DCN, the detector can predict more accurately and improve the performance of large-scale objects. However, for the small-scale and the medium-scale objects, the shape predictions may not be accurate. Enhancing feature expression with the anchor shapes will damage performance slightly. The work [35] utilizes the SAM in the backbone to pay attention to the regions of the objects, while in the ablation experiment, the application of the SAM in the head impairs performance.  
With or Without Quality: As mentioned above, quality was proposed to suppress low-quality anchor boxes produced by locations far from the center of an object. As shown in Table 2, the quality branch can boost AR from 52.9% to 54.7% and AP from 36.7% to 37.2%, improving the detection performance under all metrics. It can be noted that the methods of suppressing low-quality anchors or predictions are applied in many detectors. For example, FCOS proposes \"center-ness\" to suppress low-quality bounding boxes. Compared with the \"center-ness\", quality does not achieve a significant improvement in AP. Reviewing the decoding of bounding boxes in the DA-RetinaNet, we can conclude that predicted anchors and regression boxes determine the predictions of the bounding boxes. In training, the anchor and the box were jointly optimized and dynamically adjusted. The two-time tuning in the shape prediction stage and the regression stage may make it a good prediction for a low-quality anchor. In other words, our method is robust to the different qualities of the predicted anchors.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline *. & **SAM** & **DCN** & **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 \(\surd\) & & & 36.7 & 53.9 & 39.8 & **19.9** & **42.1** & 48.6 & **32.2** & **59.5** & 70.6 \\  & \(\surd\) & & 36.4 & 53.4 & 39.2 & 19.4 & 41.8 & 47.8 & 32.0 & 58.9 & 69.6 \\  & & \(\surd\) & **37.0** & **53.9** & **40.2** & 19.1 & 41.6 & **50.2** & 31.9 & 59.0 & **71.3** \\ \hline \end{tabular}
\end{table}
Table 1: The effects of different modules in our Feature Enhancement. *., SAM, and DCN denote without operator, spatial attention mechanism, or deformable convolution, respectively. Bold font indicates the best results.  
Center Sampling of the Region: In dynamic anchor, a location is considered a positive sample if it falls into any ground-truth box and its anchor shape target meets the regression range criterion. We obtain considerable positive samples based on the strategy above, making prediction difficult. To reduce the number of the positive samples, we used only the central portion of the ground-truth box as positive samples with the price of one extra hyper-parameter (sampling ratio). As shown in Table 3, with sampling ratio = 2, center sampling improves AP from 37.2% to 37.5%. Meanwhile, center sampling reduces the number of positive anchor boxes, which seriously affects the recall rate of the small-scale objects, decreasing by 1%.