为针对密集场景下行人检测任务中小目标检测精度不足的问题，通过提出一种基于YOLOv8模型改进的行人检测算法CSTPF-YOLOv8，通过在骨干网络融入卷积与注意力融合模块，增强对关键区域特征的提取能力，提升模型对目标细节的感知能力；优化特征融合网络，促进跨尺度特征的有效整合，提高模型对不同尺寸目标的适应性和检测精度；在低层特征图上增加检测头，利用高分辨率优势捕捉小目标；提出Focal-CIoU边界回归损失函数等方法研究了密集场景下小目标行人检测。结果表明：在WiderPerson和CityPersons数据集上的实验结果显示，与基础YOLOv8网络相比，CSTPF-YOLOv8的平均精度均值分别提高了1.4%和3.2%。可见该实验方法的有效性。

To address the issue of insufficient detection accuracy for small targets in dense scenes during pedestrian detection tasks, an improved pedestrian detection algorithm named CSTPF-YOLOv8 based on the YOLOv8 model was proposed. By integrating a convolution and attention fusion module into the backbone network, the algorithm enhanced the ability to extract features from key regions and improved the model's perception of target details. The feature fusion network was optimized to promote the effective integration of cross-scale features, thereby improving the model's adaptability and detection accuracy for targets of different sizes. Detection heads were added to the lower-level feature maps to capture small targets using the advantage of high resolution. Methods such as the Focal-CIoU boundary regression loss function were proposed to study small target pedestrian detection in dense scenes. The results show that compared to the basic YOLOv8 network, the CSTPF-YOLOv8 algorithm increases the mean average precision by 1.4% and 3.2% on the WiderPerson and CityPersons datasets, respectively, demonstrating the effectiveness of this experimental method.