Abstract
Unmanned aerial vehicles (UAVs) have been widely used in various target tracking applications through deep learning. The choice of the super parameters on the efficiency of model training and accuracy has important influence, however, the complexity of model structure and the difference of application background, the trainer cannot give precise hyperparameters. Therefore, A hyperparameter quality assessment method is proposed based on interval evidential reasoning (IER) rule in this paper. The interval confidence distribution is used to represent the interval probability generated by the uncertainty judgment, which improves the reliability of the evaluation. The evaluation process based on IER rule is defined, and the effectiveness of the evaluation is verified by experiments.
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Acknowledgment
This work was supported in part by the Postdoctoral Science Foundation of China under Grant No. 2020M683736, in part by the Teaching reform project of higher education in Heilongjiang Province under Grant No. SJGY20210456, in part by the Natural Science Foundation of Heilongjiang Province of China under Grant No. LH2021F038, in part by the graduate academic innovation project of Harbin Normal University under Grant No. HSDSSCX2022-19.
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Kang, X., Mu, Q., Han, W., Zhu, H., He, W., Huang, Z. (2023). A Hyperparameter Quality Assessment Method for UAV Object Detection Based on IER Rule. In: Fu, W., Gu, M., Niu, Y. (eds) Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022). ICAUS 2022. Lecture Notes in Electrical Engineering, vol 1010. Springer, Singapore. https://doi.org/10.1007/978-981-99-0479-2_358
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DOI: https://doi.org/10.1007/978-981-99-0479-2_358
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