Arbitrarily oriented object detection has recently attracted increasing attention for its wide applications in remote sensing. However, it is still a challenge for detection algorithms because of complex scenes, small size, rotation, densely parked. And angle discontinuity at the boundary is an important factor restricting model performance. In this paper, we propose an anchor-free approach for the high-precision detection of rotated objects. Firstly, our model achieves the classification and localization of an object by detecting its center point. Secondly, we convert the regression task of angle into a classification task and utilize the periodicity and symmetry of the transformation function to eliminate the disturbance of angle discontinuity. Thirdly, the dynamic gradient adjustment method is applied to suppress the negative effects of sample imbalance on the classification task. In addition, we proposed a union loss function to achieve accurate and stable regression of the rotated bounding box. We perform a series of ablation experiments to validate the effectiveness of the improvements. The experimental results obtained on several publicly available remote sensing datasets show that the proposed method has a higher detection accuracy, and it can be applied to efficiently identify rotated objects in remote sensing images.

Remote sensing image object detection has been studied by many researchers in recent years using deep neural networks. However, optical remote sensing images contain many scenes with small and dense objects, resulting in a high rate of misrecognition. Firstly, in this work we selected a deep layer aggregation network with updated deformable convolution layers as the backbone to extract object features. The detection and classification of objects was based on the center-point network without non-maximum suppression. Secondly, the dynamic gradient adjustment embedded into the classification loss function was put forward to harmonize the quantity imbalance between easy and hard examples, as well as between positive and negative examples. Furthermore, the complete intersection over union (CIoU) loss function was selected as the objective function of bounding box regression, which achieves better convergence speed and accuracy. Finally, in order to validate the effectiveness and precision of the dynamic gradient adjustment network (DGANet), we conducted a series of experiments in remote sensing public datasets UCAS-AOD and LEVIR. The comparison experiments demonstrate that the DGANet achieves a more accurate detection result in optical remote sensing images.