# LRP-DS: Lightweight RepPoints with Decoupled Sampling Point Set
## 5 Experiments
### Ablation Study  
Table 2 reports the results of ablation study under the PASCAL VOC07 test dataset. From Table 2, it can be found that the method of decoupling the sampling point set proposed in this paper is effective. After applying the degree of freedom decoupling, the mean average precision (mAP) can be increased from 71.4% to 73.1%. Comparing the two coordinate transformation methods proposed in this paper, the coordinate transformation function \(F_{2}\) is slightly better than the coordinate transformation function \(F_{1}\), with a maximum difference of 0.7% mAP. In addition, the experimental results in Table 2 show that LRP-DS-V2 is significantly better than LRP-DS-V1. The second decoupling method proposed in this paper can increase mAP from 72.6% to 73.3%. Therefore, this also verifies that the excessive decoupling of LRP-DS-V1 affects the object perception ability of the classification task, thereby reducing the accuracy. However, the LRP-DS-V2 can make up for this shortcoming.  
In order to verify the necessity of independent description of localization accuracy, the comparative experiment on the IoU branch is conducted. Specifically, this paper designs an IoU branch that outputs localization scores in parallel with the refine positioning branch. The IoU branch only uses a 3 \(\times\) 3 convolution, so it hardly affects the amount of calculation of the network. In the training process, in order to simplify, this paper only selects positive samples for training, and does not use more robust training methods. The robust IoU training method is complementary to the method in this paper. Table 2 shows that the RepPoints with IoU branch will not bring any benefits. However, mAP can be further increased to 73.3% for the LRP-DS with IoU branch. This proves the necessity of using classification confidence and localization scores to describe the category probability and localization accuracy of the object, respectively, in our method.  
In Table 3, the experimental results of the pseudo box convert function are compared for moment-based and min-max. For the RepPoints, the moment-based pseudo box conversion function has a better mAP. However, for our LRP-DS, the minmax can has a better mAP, with the maximum increase of 1.1% mAP. This is mainly due to the fact that our method puts more emphasis on the point set to guide sampling. Therefore, it is necessary that the point set can better fit the object edge and the semantic key point of the object. The moment-based conversion function has natural shortcomings as shown in Figure 4, so the pseudo box conversion function minmax is more suitable for the LRP-DS object detection network proposed in this paper.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Detector** & **Decouple** & **IoU** & **mAP** & **Aero** & **Bike** & **Bird** & **Boat** & **Bottle** & **Bus** & **Car** & **Cat** \\ \hline \multirow{3}{*}{RepPoints} & - & - & 71.4 & 74.0 & 80.3 & 73.1 & 62.7 & 53.9 & 79.3 & 82.9 & 85.9 \\  & - & \(\surd\) & 71.3 & 73.0 & 80.0 & 73.9 & 62.1 & 55.8 & 79.3 & 83.0 &  
Figure 6 visualizes the detection results of LRP-DS-V2. Where, the green rectangle is the boundary box of the detection object; the starting point of the yellow solid line is the center point, and the ending point of the yellow solid line is the sampling point of the localization branch \(L_{d}\); the starting point of the green solid line is the sampling point of the localization branch \(L_{d}\), and the ending point is the sampling point after the localization fine adjustment; the starting point of the red solid line is the sampling point of the localization branch \(L_{d}\), and the ending point is the sampling point after the degree of freedom conversion of the classification branch.  
Figure 6: The visualization of detection results and sample points of LRP-DS-V2.