Remote sensing object detection has important application value in fields such as environmental monitoring and resource detection and analysis. However, the current universal object detectors are not very effective in detecting remote sensing objects. To this end, this paper proposes an efficient, low-complexity and anchor-free remote sensing object detection framework YOLO-RSOD based on YOLOv7. First, an additional Tiny Object Head is proposed for better detection of micro-remote sensing objects. The original Head is then replaced with Decoupled Head (DH) to explore the detection potential of the decoupled detection head structure. Then the Explicit Vision Center (EVC) in the Centralized Feature Pyramid Network (CFP) is added to further improve the detection ability of remote sensing objects. Finally, this article also integrates a global attention module (GAM) to find attention areas in dense object scenes. Ablation experiments on the general remote sensing target detection dataset VisDrone2021 demonstrate the effectiveness of several modules introduced in this paper in remote sensing target detection. On the VisDrone2021 data set, YOLO-RSOD can achieve accuracy rates of 30.7% AP50:95 and 51.7% AP50, which are 3.1% and 3.2% higher than the baseline model respectively.