旋转目标检测是遥感图像处理领域中的重要任务，其存在的目标尺度变化大和目标方向任意等问题给自动目标检测带来了挑战。针对上述问题，提出了一种改进的RoI Transformer旋转目标检测框架：首先，利用RoI Transformer检测框架获取旋转的感兴趣区域特征(rotated region of interest, RRoI)用于鲁棒的几何特征提取；其次，在检测器中引入高分辨率网络(high-resolution network, HRNet)提取多分辨率特征图，在保持高分辨率特征同时适应目标的多尺度变化；最后，引入KLD(Kullback-Leibler divergence)损失，解决旋转目标表示的角度周期性的问题，提高检测方法对任意方向目标的适应性，并通过旋转目标边界框参数的联合优化提升目标定位精度。本文提出的旋转目标检测方法，即HRD-ROI Transformer (HRNet + KLD ROI Transformer)，在DOTAv1.0和DIOR-R两个公开数据集上与典型的旋转目标检测方法进行了比较。结果显示：相比于传统的RoI Transformer检测框架，本文方法在DOTAv1.0和DIOR-R数据集上检测结果的mAP(mean-average-precision)分别提高了3.7%和4%。

Oriented object detection is a crucial task in remote sensing image processing. The large-scale variations and arbitrary orientations of objects bring challenges to automatic object detection. An improved RoI Transformer detection framework was proposed to address above-mentioned problems. Firstly, RoI Transformer detection framework was used to obtain rotated region of interest (RRoI) for extraction of robust geometric features. Secondly, high-resolution network (HRNet) was introduced in the detector to extract multi-resolution feature maps, which could maintain high-resolution features while adapting to multi-scale changes of the target. Finally, Kullback-Leibler divergence (KLD) loss was introduced to solve angle periodicity problem caused by the standard representation of oriented object, and improve the adaptability of RoI Transformer to targets in arbitrary directions. The object localization accuracy was also improved through the joint optimization of bounding box parameters of oriented object. The proposed method, called HRD-ROI Transformer (HRNet+KLD ROI Transformer), was compared with the typical oriented object detection method on two public datasets, namely DOTAv1.0 and DIOR-R. The results show that the mean-average-precision (mAP) of detection results on DOTAv1.0 and DIOR-R datasets is improved by 3.7% and 4%, respectively.