In remote sensing and scene text images, the target has the characteristics of directional diversity and large scale change, which makes the common target detection methods have poor detection effect in these two scenes. Aiming at this problem, many specially designed detection methods have been born. Integrating the orientation angle information into the candidate area generation network or designing a special orientation angle prediction network is the mainstream method of orientation target detection, which is of great significance to remote sensing and scene text image detection. This paper summarizes the research status of rotating target detection in remote sensing and scene text. According to whether there is an anchor box or not, the current rotation detection methods based on deep learning are divided into three types: one-stage method based on anchor, two-stage method based on anchor and anchor free method, and compared from the aspects of advantages and disadvantages, backbone network and applicable scene. Finally, the development prospect and research direction of rotating target detection methods are prospected.