Compared with ordinary images, high-resolution remote sensing images have the characteristics of diverse directions and large scale changes. Aiming at the problem of remote sensing image object detection, this paper proposes an R-CenterNet remote sensing image object detection algorithm. First, redesign the CenterNet network and add a rotation factor to the network structure to provide angle information for the detection frame; secondly, increase the network depth and improve the network detection performance; finally, to aggregate the information of different regions, further extract the multi-scale information of the object. This paper proposes an attention pyramid pooling module that combines the object feature attention information with multi-scale pooling information. The experimental results show that R-CenterNet has a better detection effect, and the mAP value is increased by 8% compared with the original CenterNet detection results.