A remote sensing small target detection algorithm based on the improved YOLOv4 is proposed to address the problems of low detection accuracy and serious missing detection of small targets in remote sensing images.Firstly, the feature extraction network is improved by removing the deep feature layer to reduce semantic loss.Secondly, the lightweight attention mechanism is fused with the RFB-S structure to expand the perceptual field and enhance attention to important information, thus improving the detection precision.Finally, the Focal Loss is used to avoid the imbalance between positive and negative samples and suppress the background targets to further enhance the detection effect.The experimental results on the RSOD dataset show that the improved algorithm has an average detection precision of 96.5% and a recall rate of 87.2%, which significantly improves the detection effect and effectively avoids the phenomenon of small target miss detection, and is of great significance to small target detection in remote sensing images.