Pedestrian detection, as an essential part of object detection, has widespread applications such as automatic driving, construction safety monitoring, and so on. However, occlusion and multi-scale situation form additional difficulty to detect pedestrians. ITo tackle the difficulties posed by multi-scale and occlusion in pedestrian detection, this paper proposes an enhanced version of the You Only Look Once (YOLO) algorithm MSO-YOLO, specifically tailored for Multi-Scale and Occlusion situations. The proposed model enhances pedestrian detection performance in situations of occlusion and multi-scale objects while maintaining real-time detection. We introduce multi-scale block in the backbone for better multi-scale features extraction. In the neck of the model, we propose a global and local feature fusion mechanism which improves the ability of detecting multi-scale pedestrians by fusing global information and local information of features. We replace the original function with the improved Repulsion loss function, which strengthens the performance of the model on occlusion scenarios. In the experiment on the WiderPerson dataset, our proposed model achieved an improvement of 7.3% in mean average precision and a reduction of 8.7% in miss rate, when compared to the baseline YOLOv5 model. And it also achieves a great balance between precision and speed in comparison with other classical models.