# Knowledge-Driven Network for Object Detection
## 2 Related Work
In R3 Det [37], the authors proposed an end-to-end refined single-stage rotation detector for fast and accurate positioning of objects and designed a feature-refinement module to improve detection performance by obtaining more accurate features. The key idea of the feature refinement module is to re-encode the position information of the current refined bounding box to the corresponding feature points through feature interpolation to realize feature reconstruction and alignment. **Anchor-free approaches. ** The anchor-free detection method is a relatively new paradigm in object detection [38; 39; 40; 41], eliminating the need for anchor boxes and providing a simplified detection framework. CornerNet [14] transforms the detection of an object into the problem of detecting a pair of keypoints; this eliminates the design trouble of the anchor. It is worth noting that this method achieves high performance. CornerNet-squeeze [42] focuses on detection efficiency, using lightweight backbone networks to improve efficiency without sacrificing accuracy. CenterNet [15] notably introduces the center keypoint based on CornerNet to reduce misjudgment and improve detection accuracy. Additionally, ExtremeNet [17] detects four extreme points of the object (corresponding to the top, bottom, left, and right, respectively) to determine the location and category of the object. The anchor-free algorithm transforms object detection into a pure appearance-based keypoint estimation problem without regional classification and implicit feature learning. Referring to the idea of instance segmentation, FCOS proposes a fully convolutional one-stage object detection structure to solve object detection in per-pixel prediction fashion. Compared to YOLO, FCOS takes advantages of all points in a ground-truth bounding box to predict the bounding boxes, and the low-quality detected bounding boxes are suppressed by the proposed \"center-ness\" branch. In IENet, the authors proposed an interacting embranchment one-stage anchor-free detector for orientational objects in aerial images. In addition, they used the self-attention mechanisms to develop an IE module to force the orientation prediction task to interact with the features in the classification and localization branches to further improve the accuracy of orientation detection. **Relation for object detection. ** Many object detection methods based on deep neural networks only use internal features to classify the proposals. However, it is also very important for object detection to model the relationship between objects [43; 44; 45]. The success of the object relation module (ORM) [22] verifies the opinion that modeling between objects contributes to object detection. ORM is applied in the anchor-based object detectors. The detection effect is optimized by the relationship between objects in the image and by modeling the relationship between each ROI using geometric features and appearance features so as to obtain global auxiliary information. Based on ORM, there are corresponding extensions for different application scenarios, such as RDN [46], which can promote object detection in the video by capturing the interaction between objects in the context of time and space. Simultaneously, some networks focus on data relations. They make the relationship when labeling data. In this way, supervised training makes the relationship between objects inseparable. Another way is to use the graph convolution network to build the knowledge map and to model the relationship among the objects [47; 48]. These methods all attempt to extend the characteristics of the object by using the relationship information between the objects, which shows that it is meaningful and important to use prior knowledge to drive the object detection. The above methods achieve the preset effect for different application scenarios, but most of them are anchor-based algorithms. We believe that the relation between corner points can also be modeled in the algorithm based on an anchor-free detector.