The pedestrian is easy to be blocked and the scale is different, so the pedestrian missed detection rate is high. In view of this, the pedestrian detection algorithm based on Anchor-free idea is improved. Aimed at the problem that convolutional neural network was sensitive to target scale changes when extracting features, a fine-grained feature fusion strategy was proposed to obtain rich pedestrian feature information. The spatial attention mechanism was used to study the weight of different regions of the feature map to improve the expression ability of the model. Using multi-scale detection method, the model adaptively detected pedestrians of different scales and enhanced the robustness of model detection. The experimental results show that MR^－2 of 11.33%, 6.81%, 11.52% and 50.09% are obtained on Reasonable, Bare, Partial and Heavy subsets of Cityperson dataset, respectively, which is better than other pedestrian detection algorithms.