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.