Aiming at the limitations of small targets in remote sensing images, such as complex image background, dense distribution of small targets, and diverse target scales, this paper proposes an improved algorithm based on YOLOv8n. Firstly, a multi-scale null attention module is designed to introduce a multi-scale null attention mechanism in the backbone network in combination with the C2f module to effectively capture multi-scale semantic information and reduce the redundancy of the self-attention mechanism; secondly, a residual fast convolution module is designed to reduce the model computation and improve the feature extraction capability; finally, the PIoU v2-Iou loss function is used instead of the CIOU loss function to improve the detection accuracy of the model. The experimental results on DOTA, RSOD and VisDrone2019 datasets show that the improved YOLOv8n model improves the mAP by 2.7%, 3.3% and 3.8%, respectively, and reduces the computation by 0.5GFLOPs compared with the original model YOLOv8n, which validates the effectiveness of the new algorithm.