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International Conference on Knowledge Science, Engineering and Management

KSEM 2021: Knowledge Science, Engineering and Management pp 512–521Cite as

Finetuned YOLOv3 for Getting Four Times the Detection Speed

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12817))

Abstract

Object detection is an exciting research area in computer vision, widely used in autonomous driving, face recognition, and drones. Due to the limitation of mobile devices, the size of object detection models is limited, and it is not easy to achieve the ideal balance between detection accuracy and detection speed. So reducing the model size and increasing the model detection speed becomes a trendy research topic. The Slice-Concat structure proposed in this paper does not require too many changes to YOLOv3 and YOLOv3-SPP, and the four times improvement in detection speed is obtained only by changing the width and height of the input feature map. While getting a 4-fold FPS improvement, the network model parameters remain essentially unchanged, and the computational effort becomes a quarter of that of the original network. The improved YOLO model can also obtain a larger batch size with the same image size, providing an excellent solution for training models on hardware-constrained devices.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 61602161,61772180), Hubei Province Science and Technology Support Project (Grant No: 2020BAB012), The Fundamental Research Funds for the Research Fund of Key Lab of Traffic and Internet of Things (WUT: 2015III015-A03).

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Authors and Affiliations

  1. School of Computer Science, Hubei University of Technology, Wuhan, China

    Xin Liu & Jun Wu

Authors
  1. Xin Liu
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  2. Jun Wu
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Corresponding author

Correspondence to Jun Wu .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Han Qiu

  2. Ibaraki University, Hitachi, Japan

    Cheng Zhang

  3. University of Kentucky, Lexington, KY, USA

    Zongming Fei

  4. Texas A&M University – Commerce, Commerce, TX, USA

    Meikang Qiu

  5. Princeton University, Princeton, NJ, USA

    Sun-Yuan Kung

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Cite this paper

Liu, X., Wu, J. (2021). Finetuned YOLOv3 for Getting Four Times the Detection Speed. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12817. Springer, Cham. https://doi.org/10.1007/978-3-030-82153-1_42

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  • DOI: https://doi.org/10.1007/978-3-030-82153-1_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-82152-4

  • Online ISBN: 978-3-030-82153-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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