# Knowledge-Driven Network for Object Detection
## 2 Related Work  
**Two-stage approaches. ** The two-stage method roughly divides the object detection task into two steps. Firstly, a large number of proposal boxes are generated. Secondly, the object categories are classified and these proposals are regressed. In the first step, there are many ways to generate proposals, such as the earliest two-stage algorithm R-CNN [23], which uses a selective search [24] to extract a large number of regions of interest from images. This method is inefficient and computationally expensive. The second way that Faster R-CNN [8] generates proposals is by using a region proposal network (RPN), which sets a series of anchor sizes, then uses a sliding window to generate a large number of proposals on the feature map. In addition to the above methods, Mask R-CNN [25] adds a mask prediction branch on the Faster R-CNN. This can not only detect objects but also segment the object semantically. Cascade R-CNN [26] trains multiple cascaded detectors by using different IOU thresholds; it is an efficient object detector. Moreover, in the remote sensing scene field, traditional detectors for oriented objects are commonly used to rotate anchors based on the RCNN architecture, which multiplies the number of anchors with a variety of angles and adds rotating NMS so that the computational complexities of these models are greatly increased. The typical network is O\({}^{2}\)D-Net [27]. In visual object tracking, Siam RCNN [28] proposed a two-stage re-detection architecture, which combines a novel tracklet-based dynamic programming algorithm and object detection method to model the full history of both the object to be tracked and potential distractor objects, thereby enhancing the accuracy of current object detection and object tracking processes. In the instance segmentation field, the hybrid task cascade (HTC) method [29] proposes a new framework and effectively integrates the cascade into instance segmentation by weaving detection and segmentation features together for joint multi-stage processing. In addition, Double-Head R-CNN [30] proposes disentangling the sibling head into two specific branches for classification and regression, respectively. In contrast, the task-aware spatial disentanglement (TSD) method [31] decouples the classification and regression from the spatial dimension by generating two disentangled proposals for them, which are estimated by the shared proposal. **One-stage approaches. ** The one-stage object detectors classify and directly regress the position of the objects in a single network. Typical algorithms include YOLO [10; 32; 33], SSD [11], and RetinaNet [34]. YOLOv3 [33] directly extracts features from images through a deep neural network and produces multi-scale feature maps after fusion. Finally, it generates the bounding box of the proposals with different resolutions from the feature maps. The advantage of this scheme is that it can obtain more scale features and reduce the loss of information. In contrast to YOLOv3 [20], SSD [11] has no feature fusion and directly places the anchors densely on the input image, using features of different convolutional layers to conduct regression and classification of the anchors. RetinaNet [34] is a novel algorithm, which focuses on the design of loss function. The designed focal loss solves the problem of uneven positive and negative samples in a one-stage framework and achieves the result of the state-of-the-art MS-COCO dataset [35]. In the remote sensing field, the vehicle detection network (AVDNet) [36] proposes a new framework to robustly detect small-sized vehicles in aerial scenes. In the AVDNet work, these authors introduced ConvRes residual blocks at multi-scales to alleviate the problem of vanishing features for smaller objects caused because of the inclusion of deeper convolutional layers. In addition, AVDNet [36] proposes a recurrent-feature aware visualization (RFAV) technique to analyze the network behavior.