Aiming at the current challenges of pedestrian detection, such as complex environments, variable target sizes, and severe occlusions, which cause existing detection techniques to be prone to misjudgment and omission when recognising dense pedestrians, this paper proposes an efficient YOLOv8 improved model for dense pedestrian detection in complex scenes. DCNv2 is introduced into the backbone network, and C2f_DCNetv2 is designed to replace the C2f module, which improves the feature extraction capability of the backbone network; the detection capability of the model for small targets is improved by adding small-target detecting heads to the architecture, which improves the accuracy of small-target detection and recognition; based on the four detecting heads as well as the AFPN, the AFPN-4H is designed, which optimises the information fusion between the feature layers and improves the model′s adaptability and detection accuracy for targets of different scales; finally, through the combination of Wise-IoU, Focaler-IoU, and MPDIoU, the WFM-IoU is obtained, which further improves the target localisation accuracy. The experimental results show that compared with the original YOLOv8n model, it improves 1.6, 4.0, 3.6 and 3.8 percentage points in the key indexes of P, R, AP50 and AP50:95, respectively, which are also inferior to other algorithms. The improved algorithm in this paper has better performance in the dense pedestrian detection task in complex scenes.