# DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images
## 1 Introduction
[37] points out that Focal Loss defines a static ratio coefficient to increase attention to hard examples during training, which does not adapt the change of data distribution. In this context, the article propose a gradient harmonizing mechanism (GHM) to restrict the disharmony. In the field of remote sensing, there is a large number of hard examples in remote sensing images because of illumination, shadow, shelter, haze and so on. Referring to the experimental results, we define these indistinguishable examples as outliers because they exist stably even after the model is converged. The schematic diagram of outliers is shown in Figure 1. We introduce the gradient harmonizing method into the Centernet to improve the performance of the model. However, the gradient harmonizing mechanism doesn't consider the imbalance between positive examples and negative examples, and the inhibition mechanism of example imbalance needs to be further improved. We propose a dynamic gradient adjustment anchor-free object detection method used for optical remote sensing field. The model is based on CenterNet [36], and the deep layer aggregation [38] combined with deformable convolution layers [39] was chosen as the backbone network to extract effective features of objects. In order to settle the problem of  
Figure 1: Illustrations of object classification in deep learning, Colors are used to distinguish different categories of objects. (**a**) Under ideal conditions. (**b**) Outliers in the sample. example imbalance and improve the performance of the detector, we propose a dynamic gradient adjustment network which embeds the gradient density into the updated Focal Loss function. Furthermore, the Complete-IoU loss function [40] is used for the regression of bounding box, which can harmonize the coupling characteristic of border size and its location. Contrast experiments were conducted to compare the performance of the proposed method with other advanced detectors on dataset UCAS-AOD and LEVIR. The experimental results show that the proposed network significantly improves the accuracy of object detection. The main contributions of this article are listed as follows:  
1. We propose a dynamic gradient adjustment method to harmonize the quantity imbalance between easy and hard examples as well as between positive and negative examples. 2. For the detection of small and dense objects in optical remote sensing images, we select a deep layer aggregation combined with deformable convolution as the backbone network, which has a better prediction performance. 3. We select the Complete-IoU as regression loss function instead of the sum of dimension loss and position offset loss to improve learning efficiency. 4. The object detection precision and recall in optical remote sensing images demonstrate the significant improvement of our method. The rest of the article is organized as follows. Section 2 reviews relevant works in remote sensing object detection and describes basic principles of CenterNet. We expound the proposed improved method in Section 3. The effectiveness of the proposed method is verified and comparison experiments with the existing detectors are conducted in Section 4. Finally, Section 5 gives the conclusion of this article.