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International Conference on Artificial Neural Networks

ICANN 2021: Artificial Neural Networks and Machine Learning – ICANN 2021 pp 34–45Cite as

Pruned-YOLO: Learning Efficient Object Detector Using Model Pruning

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12894))

Abstract

Accurate real-time object detection plays a key role in various practical scenarios such as automatic driving and UAV surveillance. The memory limitation and poor computing power of edge devices hinder the deployment of high performance Convolutional Neural Networks (CNNs). Iterative channel pruning is an effective method to obtain lightweight networks. However, the channel importance measurement and iterative pruning in the existing methods are suboptimal. In this paper, to measure the channel importance, we simultaneously consider the scale factor of batch normalization (BN) and the kernel weight of convolutional layers. Besides, sparsity training and fine tuning are combined to simplify the pruning pipeline. Notably, the cosine decay of sparsity coefficient and soft mask strategy are used to optimize our compact model, i.e., Pruned-YOLOv3/v5, which is constructed via pruning YOLOv3/v5. The experimental results on the MS-COCO and VisDrone datasets show that the proposed model achieves a satisfactory balance between computational efficiency and detection accuracy.

This work is supported by Chinese National Natural Science Foundation (62076033, U1931202), and BUPT innovation and entrepreneurship support program (2021-YC-T026).

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Notes

  1. 1.

    https://github.com/ultralytics/yolov3, https://github.com/ultralytics/yolov5.

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Authors and Affiliations

  1. School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China

    Jiacheng Zhang, Pingyu Wang, Zhicheng Zhao & Fei Su

  2. Beijing Key Laboratory of Network System and Network Culture, Beijing, China

    Jiacheng Zhang, Pingyu Wang, Zhicheng Zhao & Fei Su

Authors
  1. Jiacheng Zhang
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  2. Pingyu Wang
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  3. Zhicheng Zhao
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  4. Fei Su
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Corresponding author

Correspondence to Zhicheng Zhao .

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Editors and Affiliations

  1. Comenius University in Bratislava, Bratislava, Slovakia

    Igor Farkaš

  2. iMotions A/S, Copenhagen, Denmark

    Paolo Masulli

  3. University of Tübingen, Tübingen, Baden-Württemberg, Germany

    Sebastian Otte

  4. Universität Hamburg, Hamburg, Germany

    Stefan Wermter

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Zhang, J., Wang, P., Zhao, Z., Su, F. (2021). Pruned-YOLO: Learning Efficient Object Detector Using Model Pruning. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12894. Springer, Cham. https://doi.org/10.1007/978-3-030-86380-7_4

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  • DOI: https://doi.org/10.1007/978-3-030-86380-7_4

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