A vehicle detection method based on Taylor expansion model pruning algorithm

Zheng Li; Zhuo Yan; Anyan Xiao; Huangxin Xu; Yucheng Cao; Chaofan Jin; Xinyu Zhong; Shuotong Zhang; Changyu Zhao

doi:10.1117/12.3021328

19 February 2024 A vehicle detection method based on Taylor expansion model pruning algorithm

Zheng Li, Zhuo Yan, Anyan Xiao, Huangxin Xu, Yucheng Cao, Chaofan Jin, Xinyu Zhong, Shuotong Zhang, Changyu Zhao

Proceedings Volume 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023); 130632G (2024) https://doi.org/10.1117/12.3021328
Event: Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 2023, Changchun, China

Abstract

In order to solve the problems of large number of deep model parameters and poor real-time performance in the existing vehicle detection methods, this paper proposes a vehicle detection method based on Taylor expansion model pruning algorithm. VGG16 is the main trunk network, the Taylor expansion method is is used for filter pruning, and the Lagrange algorithm is combined. Experiments show that the vehicle detection method proposed in this paper can reduce the complexity of the model, reduce the number of model parameters by 90%, and shorten the detection time without affecting the detection accuracy, which has certain application and promotion value.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zheng Li, Zhuo Yan, Anyan Xiao, Huangxin Xu, Yucheng Cao, Chaofan Jin, Xinyu Zhong, Shuotong Zhang, and Changyu Zhao "A vehicle detection method based on Taylor expansion model pruning algorithm", Proc. SPIE 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 130632G (19 February 2024); https://doi.org/10.1117/12.3021328

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