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Aiming at the problem of low detection accuracy caused by complex background and small objects in remote sensing imageries, a remote sensing imagery object detection algorithm PARF-FCOS based on FCOS network is proposed. The algorithm constructs a position attention module, and uses the module to reconstruct the feature extraction network to enhance the ability of the network to extract target information; In the feature fusion stage, RFB(receptive field block) is used to enhance the shallow feature map, and the target context information is used for auxiliary judgment to improve the detection ability of the network for small-scale objects; During training, DIOU loss(distance intersection over union loss) is introduced for boundary box regression. By optimizing the distance between the center point of the target box and the prediction box, the regression process is more stable and accurate. Experiments are carried out with public dataset DIOR. Compared with the original FCOS, the mean Average Precision of the algorithm is improved by 4.3%, up to 70.4%, and the detection speed is 23.2 FPS.