The pyramid structure of the YOLOv5 network model can not effectively capture the cross-scale feature information of the object and the gradient disappears which causes the problem that the accuracy of object detection is not high. The attention mechanism of FPT(feature pyramid transformer) can be used to extract the cross-scale feature information of the object, and then the FPN(feature pyramid network) and the PAN(path aggregation network) in the original YOLOv5 network model are replaced by the FPT. Skip connection is added at both ends of the FPT structure and an improved Mish activation function is introduced. Thus, an improved object detection network model, namely YOLO FS, is proposed. The experimental results on PASCAL VOC and MS COCO datasets show that the average detection accuracy, recall and F1-score of object detection based on the proposed network are significantly improved.