The dense crowd detection algorithm is of great significance in fields such as public safety monitoring and intelligent traffic scheduling. Aiming at the problems of target occlusion, low detection accuracy of small targets, and missed detection in dense scenes, this paper, based on the lightweight YOLOv10, proposes an improved YOLOv10-SCD algorithm. First, the convolutional block attention module （CBAM) is integrated. Through channel-space two-dimensional weighting, the motion blur processing ability is enhanced and the pedestrian detection accuracy is improved. Second, the dynamic sample (DySample) up-sampler is introduced to improve the image resolution and processing efficiency. At the same time, the SIoU optimization loss function is adopted to further improve the positioning accuracy and the bounding box regression speed. Finally, the performance of the algorithm is verified on the dense crowd dataset, and the role of each module is analyzed through ablation experiments. Experiments show that compared with the original YOLOv10, the core indicators of YOLOv10-SCD are significantly improved:the precision is increased by 1.5%, the recall rate is increased by 2.9%, mAP@0.5 is increased by 0.8%, and mAP@0.5:0.95 is increased by 1.8%. The ablation experiments are carried out on two sets of datasets,the self-built dataset focuses on analyzing the individual and synergistic effects of each module on the algorithm accuracy and efficiency; the WiderPerson public dataset verifies the generalization ability of the algorithm. Therefore, YOLOv10-SCD can efficiently cope with the complex scenes of dense crowds, alleviate the problems of target occlusion and difficult recognition of small targets, and significantly improve the robustness and comprehensive performance of target detection.