Target detection is an important prerequisite of automatic driving and an important link of interaction with external information. Aiming at the problem of low accuracy and missing detection of distant pedestrians at night,a nighttime pedestrian recognition model of YOLOv5 p4 for detecting small sized pedestrians is proposed in this paper. Firstly,by adding a detection layer of smaller targets and introducing a BiFPN feature fusion mechanism to prevent small targets from being drowned by noise,the network model can be more focused on the small features of the object. At the same time,K means prior frames are used to cluster anchor frames of smaller targets,and multi scale data enhancement method is used to increase the robustness of the model. MetaAcon C activation function and EIoU regression loss function are used to improve the model convergence effect and improve the accuracy of long distance pedestrian detection algorithm. Finally,the improved YOLOv5 p4 model for pedestrian detection is verified on the infrared pedestrian data set FLIR. The experimental results show that compared with the traditional methods,the accuracy of this method is improved from 86.9% to 90.3%,which is suitable for pedestrian detection in infrared images.