Small target detection is an extremely challenging task in computer vision, where existing detection algorithms suffer from high complexity, large computational overhead, and low detection accuracy, leading to issues such as missed detections and false alarms. In this paper, the LDF-YOLO algorithm is proposed to enhance detection accuracy and decrease missed detection rates for small objects. Firstly, improvements are made to the Head section by introducing a feature transformation module in the feature fusion network and designing the LP-Detect detection head tailored for small objects. Secondly, drawing inspiration from residual gated mechanisms and local feature enhancement strategies, the LR-C2f module is devised to bolster the model′s capability in extracting local features. Finally, the local feature enhancement module is integrated to enhance backbone′s ability to extract information from small objects. On the publicly available Tiny Person dataset, LDF-YOLO outperforms the original YOLOv8 by achieving a 4.5% improvement in mAP0.5 and a 5.5% increase in recall. Experimental results validate the effectiveness of our proposed improvements. Furthermore, generalization comparison experiments on the NWPU VHR-10 and VisDrone2019 datasets demonstrate improvements across all metrics.