Based on the rapid development of the unmanned field, in order to improve the speed and accuracy of road pedestrian target detection, a road pedestrian target detection method based on YOLOv5 network improved by YW-YOLO is proposed, which is changed into RepGFPN in the YOLOv5 model"s neck structure, which fully exchanges the high-level semantic information and the low-level spatial information, adds the adaptive fusion mechanism, introduces the SimAM attention module mechanism, which improves the feature extraction ability of the algorithm, and in terms of loss function, Optimal Transport Assignment is used to optimize the loss function, and the experimental results show that the proposed algorithm in this paper, compared with the original algorithm, the recognition accuracy rate on the road pedestrian category dataset is improved from 38.1% to 52.6%, which has a better detection effect.