# LRP-DS: Lightweight RepPoints with Decoupled Sampling Point Set
## 1 Introduction  
Object detection is one of the most widely used tasks in computer vision. Current object detection methods can be roughly divided into two categories according to whether a priori anchor is needed: anchor-based object detection, and anchor-free object detection. Anchor-based object detection applies a large number of prior anchors to fit the boundary box of a real object, such as the famous single-stage object detection methods of YOLO [1], SSD [2], and the two-stage object detection algorithm represented by Faster R-CNN [3]. The prior anchor is usually set manually according to the statistical characteristics of the dataset. In essence, the anchor can provide a reference for the network, so that the network can obtain some prior knowledge. Therefore, in the training process of network, it is easy to learn the mapping relationship between the anchor and ground truth. In the process of reference, the predicted object box can be obtained according to the prior anchor and the offset calculated by the network. By contrast, anchor-free object detection, which has attracted a lot of attention recently, does not rely on a prior anchor, such as CornerNet [4], ExtremeNet [5], representative points network (RepPoints) [6], etc. RepPoints directly regards the bounding boxes of object as the regression of the point set. The innovation of RepPoints is to use the set of representation points to describe the object. When the flexible representation method is applied to object detection, the set of representation point can adapt to the geometric changes of the object and provide guidance for feature extraction. However, RepPoints ignores the difference between the objectclassification and localization task. Compared with the geometric boundary information of the object, the classification task pays more attention to the semantic key points of the object. For example, for recognition of a cat, the localization task is suitable for extracting features at the boundary of the cat, while the classification task prefers to extract features at semantic key points such as the cat's eyes and nose. In the original RepPoints, the classification and localization tasks share the same sampling positions, which is not appropriate. Therefore, this paper proposes to decouple the feature sampling positions of object classification and localization, so as to give the sampling positions of feature in the classification branch a certain degree of freedom. This allows the feature sampling points of classification to actively find the semantic key position of the object, to improve the recognition accuracy of the classification task. In the general post-processing method non maximum suppression (NMS), the bounding box with the highest classification confidence is selected in the same category, and then filters out the detection results where the calculated value of the intersection over union (IoU) overlap is greater than the certain threshold. The above steps are repeated. Therefore, in traditional object detection methods, the classification confidence is potentially expected to describe the classification probability and localization accuracy. However, many studies (such as IoU-Net [7]) show that the classification confidence is not enough to describe the localization accuracy of the bounding box. This is considered a mismatch between classification confidence and localization accuracy, due to the double responsibility of classification confidence. On the basis of RepPoints, the problem of mismatch is more serious after the decoupling of feature sampling points between classification and localization. Therefore, the classification confidence is used to describe the category probability of the bounding box, and the localization score is introduced to describe the localization accuracy of the bounding box.