# Knowledge-Driven Network for Object Detection
## 1 Introduction  
With the rapid development and remarkable achievements of convolutional neural networks in the field of computer vision, CNNs [1; 2; 3; 4] have penetrated many fields of computer vision, such as image classification [5; 6; 7], object detection [8; 9; 10; 11], and pose estimation [12; 13]. As one of the most fundamental computer vision tasks, object detection has resulted in a surge of research interests as it can be easily applied to real-world applications in intelligent cities, intelligent transportation, and other fields. The task of object detection is essentially the extraction of relevant object features from an input image, finding out the objects' regions of interest from these object features, and classifying these while returning to the location of the objects from the region of interest. However, there are various kinds of objects in aerial images. These objects have different appearances, shapes, postures, and there are some interference factors (e.g., illumination and occlusion), which make the detection more difficult. Therefore, object detection is a challenging and non-trivial task. At present, most of the mainstream high-performance object detectors follow the anchor-based frameworks [8; 9]. Firstly, they generate object proposals and then classify and regress each proposal independently. This is a general method based on the two-stage framework, such as Fast R-CNN [9] and Faster R-CNN [8]. It is worth mentioning that anchor-free object detectors (e.g., CornerNet [14], CenterNet [15; 16], and ExtremeNet [17]) have appeared in recent years. These change the original inherent object detector that follows the anchor-based paradigm, eliminating the need for designing anchor boxes. These issues of detecting objects are turned into a problem of detecting a pair of keypoints. In addition, the methods adopt corner pooling to help the CNN locate keypoints with more accuracy. A single convolution network is used to predict the heatmaps, and then the embedding and the offset are performed independently for each class of channels. The heatmaps contain the category and location information of objects. Each channel corresponds to a category of heatmaps. The results of anchor-free methods have shown that detecting corners is the most challenging task. Therefore, improving the accuracy of the heatmap becomes a significant issue for an anchor-free framework. Currently, most of the anchor-free object detection algorithms independently detect objects, but if the model can learn the relationship between objects, this will clearly help to improve the detection effect. Therefore, the use of the relative positional relationship concerning the object in the anchor-free algorithm is a noteworthy direction. For many years, researchers in the computer vision field have believed that contextual information or relationships between objects contribute to object recognition [18; 19; 20; 21; 22]. The basic idea is to take the relational features of an object as the weights of the apparent features of other objects in the image and measure them. This method validates the advantages of modeling objects and creates a general module, which can improve the accuracy of the anchor-based algorithm on the original basis. However, as the anchor-free algorithm does not have the design of an anchor, it is unable to generate a lot of proposals representing the appearance feature of the object and cannot directly measure the relationship between heatmaps. Therefore, it is a very challenging problem to explore the target relationship in heatmaps. In the literature [22], there is strong evidence that object relations can be used to support various visual tasks (e.g., image recognition and object detection). Inspired by the relation network, KDNet proposes a new knowledge-driven network to solve the relationship between objects, shown in Figure 1. The two objects are classified as the same object because of the use of an anchor-free network.