# Object Detection in Drone Imagery via Sample Balance Strategies and Local Feature Enhancement
## 3 Methods
### Feature Extracting Module and Offset-Head  
We apply the Resnet101 [53] as the backbone and Feature Pyramid Networks (FPNs) [20] as the neck part section for the detector. The module of the feature maps extraction module is the basic part of the framework. Usually, FPN uses a top-down architecture, connected horizontally, to build intra-network feature pyramids from a single scale of input. The FPNs contains two parts: A down-top module and a top-down module. Extracted from the original image, the feature maps which have the same scale are called a stage. The last feature maps are stored in the stage of the bottom-top module. For When it comes to the detection of objects detection, FPN is task-independent and each level of the pyramid in the FPN is used to detect objects at a specific scale. In total, There are four feature maps of different sizes totally are generated. After adding the up-sampled feature map with the feature map after that underwent \(1\times 1\) convolution, each feature map then goes through a \(3\times 3\) convolution layer, respectively, to eliminate the any negative impact that may have been caused by direct summation. Assume that these five feature maps levels, sorted from largest to smallest in size, are P3, P4, P5, P6 and P7. Then all the feature maps are assembled as and work as the features fed into the next module, just as is shown in Figure 2.  
In this paper, the framework we proposed is designed based on the fully convolutional one-stage object detector, and it lets \(\Gamma_{i}^{*}\in(R)^{(H\times W\times C)}\) be the feature maps at pyramid level \(i\). On each feature pyramid, the anchor-based approach places anchor points uniformly on each \(H\times W\) spatial location, and the training target is determined by calculating the IoU overlap between all the anchor points and the ground truth box. Finally, the objectives are optimized using pyramid features. Besides, we also proposed a regression branch as the head part section of the anchor-free detector based on the idea of pixel-wise prediction (e.g.,  
Figure 1: Framework of object detection from images captured by drones. Figure 2: The framework of the feature extracting module. FCOS, Fovea), as shown as in Figure 3. The idea of pixel-wise prediction is similar with to the algorithms of semantic segmentation, as the core is the method of dense prediction. The detector based on pixel-wise predictions can avoid complex calculations related to the anchor box, such as overlapping issues when training the model. In addition, it, and avoids introducing hyper-parameters related to the anchor box, which are often very sensitive to the final detection results performance. On each FPN layer \(F_{i}^{*}\), the anchor points are uniformly distributed uniform. The Figure 3a shows the detailed structure of Head (regression branch), and (b) defines shows the definition of the four variants. The white points in (b) are the anchor points in some FPN layers, and the blue point is the center point of the ground truth object. The offset between anchor point and center point is defined as \((\triangle x,\triangle y)\), and the width and height of the predicted box are defined as \((\triangle w,\triangle h)\). The distance between the anchor point and center points can be calculated as: Distance=\(\sqrt{\triangle w^{2}+\triangle y^{2}}\). By Through the regression branch, we can directly obtain the distance directly without needing any additional calculations. Thus the predicted boxes are regressed, a. Moreover, for each ground truth object box, each anchor regresses the corresponding offset, width and height. When there are multiple ground-truth object boxes mapped with one anchor, the anchor with the lowest distance from the center point of the object box will be kept. Specifically, each center point of in the ground-truth object box is defined as \((x^{c},y^{c})\), and the width and height are \(w^{c}\) and \(h^{c}\) respectively.