Given that infrared pedestrian-vehicle images are difficult to detect due to their low resolution, poor quality, and high noise, an infrared image pedestrian and vehicle target detection algorithm based on YOLOV8 is proposed, namely PSWG-YOLO. For the YOLOv8n network, a 160×160 maximum feature map P2 is added to improve the model's detection ability of pedestrian small targets. At the same time, the SPD-Conv part is used to replace the stride-2 convolutional layer of the original network to improve the feature extraction capability of low-resolution images. In addition, the loss function is replaced with WIoU to optimize the model's processing of low-quality images. Finally, the Ghost module is introduced to reduce model complexity. The experimental results show that the improved PSWG-YOLO algorithm significantly reduces the model volume and parameter amount while ensuring high detection accuracy. Compared with the original YOLOv8n algorithm, the P, R, and mAP@0.5 on the public infrared data set FLIR_v2 are increased by 1.6 %, 6.3 %, and 7.2 % respectively, and the number of parameters is reduced by 16 %, and the model size is reduced by 15.8 %, which improves the accuracy of the pedestrian-vehicle detection in infrared scenarios and is easy to deploy.