# Object Detection in Drone Imagery via Sample Balance Strategies and Local Feature Enhancement
## 2 Related Work
### Object Detection  
In the object detection task, we can classify the detector according to whether the algorithm uses anchors to generate candidate target boxes, which can usually be divided into two types: Anchor-based and anchor-free. The anchor-based detector can be divided into one-stage and two-stage according to the algorithm flow. Moreover, the anchor-free detector mainly contains keypoint-based and segmentation-based methods. Anchor-based detector: An anchor (also known as an anchor box) is comprised of a set of rectangular boxes that are clustered on the training set. The boxes use k-means before training, which represents the aspect scale of the main distribution of the targets in the dataset. During the inference, the n-candidate rectangular boxes are extracted from these anchors on the feature map, and are generated before undergoing further classification and regression. There are two kinds of detector: Two-stage method and one-stage method. The two-stage method first uses the algorithm to generate a series of candidate boxes, and then these are classified and regressed using the convolutional neural network. We can take Faster R-CNN [14] as an example, as it consists of a separate region proposal network and a region-wise prediction network to detect objects. Lots of algorithms are proposed based on the idea of Faster R-CNN. The authors of [15; 16] propose to improve the training strategy and reform the loss function. The authors of [17; 18] propose to redesign the architecture of the detection method. The authors of [19; 20] propose the method on feature fusion and enhancement. The authors of [21, 22] propose improving the balance and proposal aspects during training. The one-stage method is the end-to-end algorithm which directly regresses the class and position. It is faster than the two-stage method and only a little less accurate. The one-stage anchor-based detector receives lots of attention when working with the proposed SSD [23]. The authors of [24] introduce new loss function to improve the accuracy. The authors of [25, 26] propose to enrich the feature and align the different domains. At present, the performances of the methods based on the one-stage anchor-based detector and the two-stage anchor-based detector are very close. Anchor-free detector: As the name implies, anchor-free does not require setting the anchor's aspect ratio in advance, including the keypoint-based method and segmentation-based method. Due to this simple network structure, it is probably more suitable for industrial applications. The keypoint-based method first locates a few key-points generated by the pre-learned procedures to create bounding boxes for objects. CornerNet [27], defining two key-points (top-left corner and bottom-right corner) to represent the bounding box of objects, is the most representative of keypoint-based methods. At the same time, the CornerNet-Lite [28] method is the lite version improving its pace. CenterNet [29] extends CornetNet as a triplet (top-left corner, bottom-right corner and center) to improve performance. ExtremeNet [30] introduces five key points (top, left, bottom, right, and center) to generate objects' bounding boxes. In pose estimation, 3D object location, and orientation identification, the key-point-based detector is the basic module, regressing other properties by center points in [31]. In RepPoint [32], it studies the deformable convolution and avoids the problem of no matching, despite key-points based detector, not like CornerNet or ExtremeNet. The segmentation-based method is similar to the instance-segmentation algorithm. It tends to find any positive sample pixel in the detection frame and directly predict the bounding box's four regression values (top, bottom, left, and right) by the full convolution branch. YOLO [33] is an early method that divides the picture into \(n\times n\) grids. Each grid cell contains the center point of a target and then detects the objects. In DenseBox [34], the positive samples are defined as located in the center of the object.