Abstract: In the subway scene, small pedestrian targets contain less feature information due to their low resolution, and it is still challenging for object detectors to detect such objects at this stage. The SSD target detection algorithm uses the multi-scale detection head of the pyramid network, which can improve the pedestrian target detection performance to a certain extent, but it still has certain limitations in applying it to small pedestrian target detection in complex environments such as subways. In view of the above problems, this study proposes an improved SSD algorithm to enhance the detection effect of small pedestrian targets in subway scenes. Construct a dataset of pedestrian targets in subway scenes, mark the corresponding labels, and perform data preprocessing operations at the same time. In this study, a pyramid feature enhancement module is added to the feature extraction network, and the multi-branch residual unit, sub-pixel convolution and feature pyramid are combined to obtain image multi-scale and multi-receptive field fusion features. Use the context information fusion module to fuse the low-level features of the image with the context features to generate an extended feature layer for detecting small pedestrian targets, and design an Anchor-free dynamic positive and negative sample allocation strategy to generate optimal positive samples for small pedestrian targets . A dynamic positive and negative sample allocation strategy based on Anchor-free is designed to generate optimal positive samples for small pedestrian targets. The experimental results show that the proposed improved SSD algorithm can effectively improve the performance of small pedestrian target detection in subway scenes, and the effect of small pedestrian target detection with severe occlusion is more obvious.