In dense pedestrian scenes, severe occlusions, numerous small targets, significant scale variations, and complex environments often lead to missed detections, false detections, and inaccurate localization of pedestrians. To address these challenges, this paper proposes a lightweight dense pedestrian detection algorithm DC-YOLO. The algorithm is based on YOLO11n. In the backbone network, a lightweight feature extraction network, EfficientNetV2S-S3, is proposed to enhance the model′s feature extraction capability for small and multi-scale targets while reducing model parameters and computational costs. In the neck network, the P-LightNeck module is proposed to further improve the feature fusion capability for small targets, achieving collaborative optimization of detection accuracy and efficiency. The RepNCSPELAN4 convolutional module is introduced to strengthen the feature extraction capability for occluded targets through multi-scale convolution and re-parameterization techniques, while improving inference efficiency. A dynamic multi-scale collaborative attention module, DynaMSAttn, is designed to enhance the model′s adaptability to targets of varying scales and complex environments. Experimental results show that, compared to YOLO11n, the DC-YOLO algorithm achieves improvements of 4.7% in mAP@0.5 and 4.5% in mAP@0.5-0.95 on the CrowdHuman dataset, while reducing the parameter count by 46.2%. Comparative experiments and ablation experiments verify that the DC-YOLO algorithm exhibits excellent detection performance and robustness in dense pedestrian detection tasks.