基于正交优化策略的YOLO模型超参数优化方法
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贵州大学

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TP391.41

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国家自然科学基金(62163007,62373116),贵州省科技计划(黔科合平台人才[2020]6007-2,黔科合支撑[2021]一般439)


Hyperparameter optimization of YOLO model based on orthogonal optimization strategy
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Guizhou University

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    摘要:

    为实现YOLO模型的超参数自动优化,本文提出基于正交优化策略的YOLO模型超参数优化方法(hyper-parameter optimization of YOLO model based on orthogonal optimization strategy, OOS)。首先基于统计学的正交试验原理,提出了种群的正交搜索方法与超参数贡献度分析策略,提高了算法的优化效率;然后,设计了均匀正交搜索策略和邻域正交搜索策略,以缓解YOLO模型陷入局部最优和早熟收敛问题。最后,在NWPU VHR-10和Pascal VOC两个目标检测数据集上,以YOLOv5、YOLOv5s-Transformer和YOLOv7为优化对象进行测试,测试结果表明,本文提出的OOS超参数优化方法对于YOLO模型的识别精度均有所提升。在两个数据集上的平均识别精度mAP@0.5分别提升至93.94%、93.18%、93.45%以及85.81%、84.59%、90.62%;mAP@0.5-0.95提升至60.00%、60.08%、56.98%以及62.27%、58.89%、71.91%,可为目标检测模型的超参数智能优化提供一种新方法。

    Abstract:

    In order to realize the automatic optimization of hyperparameters of YOLO model, the hyperparameter optimization of YOLO model based on orthogonal optimization strategy (OOS) is proposed. Firstly, based on the principle of statistical orthogonal test, the orthogonal search method of population and the hyperparameter contribution analysis strategy are proposed to improve the optimization efficiency of the algorithm. Then, the uniform orthogonal search strategy and the neighborhood orthogonal search strategy are 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.

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杨青华,杨观赐,钟世昊. 基于正交优化策略的YOLO模型超参数优化方法[J]. 科学技术与工程, , ():

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  • 收稿日期:2023-12-05
  • 最后修改日期:2024-06-21
  • 录用日期:2024-06-24
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