# DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images
## 1 Introduction  
With the rapidly developing technology applied in the remote sensing field, target detection in remote sensing images has been a popular area of research because of its wide applications [1; 2; 3; 4; 5]. Meanwhile, object detection in optical remote sensing is faced with various challenges, including changes in the appearance of objects caused by illumination, observation angle, shadow, weather, and partial shelter. These challenges result in a relatively lower accuracy and higher computational complexity compared with general object detection methods. In recent years, deep convolution neural networks (CNNs) have been used by researchers to detect objects, and a variety of methods have been proposed to achieve better performance [6; 7; 8; 9; 10; 11; 12; 13; 14]. The excellent performance of CNN arouses the research interest of scholars. Detection methods based on CNN have been applied in fields such as image denoising, object detection, semantice division, pose estimation, and object tracking in natural scene images [15; 16; 17; 18]. Meanwhile, some improved CNN methods have been applied in optical remote sensing images to promote performance and applicability [1; 19; 20; 21]. Besides, a new set of methods based on Capsule network are proposed for object detection [22; 23; 24; 25; 26], which may offer another alternative solution for optical remote sensing images as an emerging research area. In general, typical deep convolutional neural networks can be divided into anchor-based and anchor-free according to the regression of bounding box. For the anchor-based framework, the regression of the bounding box relies on a set of pre-defined anchor boxes with various sizes and aspect ratios. Detect networks such as Faster R-CNN [27], SSD [14], YOLOv2 [28] and YOLOv3 [29] are of the anchor-based framework. This type of methodneeds to select a certain number of anchor boxes on the feature layer, then it calculates the confidence of objects and refines bounding boxes according to the intersection over union (IoU), and finally draws detection results including these refined bounding boxes and confidences on original images. However, the use of the anchor box suffers from two drawbacks. First, the network needs a certain number of preset anchor boxes to match different objects with various scales and aspect ratios, while most of the preset boxes are invalid that would increase the training time of the model [30]. Second, the anchor-based method introduces many hyper-parameters that need to be optimized [14; 30], which increases the difficulty of model design. Due to the emergence of Feature Pyramid Networks [31] and Focal Loss [30], some scholarly research has been performed to investigate anchor-free network frameworks. The networks directly detect objects without presetting anchor boxes, which is conducive to the design of the network and the improvement of training speed. Anchor-free detectors can be divided into two different types. The first type delineates the bounding box by merging a pair of corner points of the target. This kind of anchor-free detector, such as CornerNet [32; 33] and MatrixNets [34], is classified as the keypoint-based network. The other type locates the center point of the targets and then predicts the width and height of the bounding box. This kind of anchor-free detector is called a centerpoint-based network [35; 36]. The CenterNet [35] doesn't need to preset a large number of anchor boxes and has achieved similar or even better performance compared with anchor-based detectors. Centerpoints appear inside the object while corners are outside, so the extraction of centerpoints is more accurate. The centerpoint-based detectors have more advantages in object detection. Focal Loss is used to solve the class imbalance between foreground and background as well as between easy and hard examples. However, [PERSON] et al.