In order to realize the automatic optimization of hyperparameters of YOLO model, the hyperparameter optimization of you only look once (YOLO) model based on orthogonal optimization strategy (OOS) was proposed. Firstly, based on the principle of statistical orthogonal test, the orthogonal search method of population and the hyperparameter contribution analysis strategy were proposed to improve the optimization efficiency of the algorithm. Then, the uniform orthogonal search strategy and the neighborhood orthogonal search strategy were designed to alleviate the problem of the YOLO model falling into the local optimum and premature convergence. Finally, YOLOv5, YOLOv5s-Transformer and YOLOv7 were used as optimization objects to test on two target detection datasets, NWPU VHR-10 and Pascal VOC. Test results show that the recognition accuracy of the YOLO model is improved by the OOS hyperparameter optimization method in all cases. The average recognition accuracy mAP@0.5 on two datasets is improved to 93.94%, 93.18%, 93.45%, and 85.81%, 84.59%, 89.96%. The mAP@0.5-0.95 is improved to 60.00%, 60.08%, 56.98%, and 62.27%, 58.89%, 70.77%. It can provide a new intelligent method for hyperparameter optimization of object detection model.