Small object detection has important application value in the fields of environmental monitoring, resource detection and analysis, etc. However, the current general object detectors are not very ideal for the detection of small objects. To this end, this paper proposes an efficient, low-complexity, anchor-free small object detection framework YOLO-SOD based on YOLOv8. First, a content-aware feature recombination upsampling operator (CARAFE) is integrated in the upsampling operation part of the framework to achieve more accurate and efficient feature reconstruction. Then, the spatial and channel reconstruction convolutional block (SCConv) is widely integrated in BackBone and Neck to reduce the computational cost caused by redundant feature extraction in visual tasks. Finally, the occlusion-aware attention module (SEAM) is introduced at the end of the detection framework to help the model more accurately identify occluded objects. The ablation experiment on the general small object detection dataset VisDrone2021 proves the effectiveness of several modules introduced in this paper for small object detection. On the VisDrone2021 dataset, YOLO-SOD can achieve an accuracy of 31.1% AP50:95 and 52.3% AP50, which are 2.5% and 3.1% higher than the baseline model respectively.