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A Self-attention Network for Face Detection Based on Unmanned Aerial Vehicles

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Intelligent Robotics and Applications (ICIRA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13456))

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Abstract

Face detection based on Unmanned Aerial Vehicles (UAVs) faces following challenges: (1) scale variation. When the UAVs fly in the air, the size of faces is different owing to the distance, which increases the difficulty of face detection. (2) lack of specialized face detection datasets. It results in a sharp drop in the accuracy of algorithm. To address these two issues, we make full advantage of existing open benchmarks to train our model. However, the gap is too huge when we adapt face detectors from the ground to the air. Therefore, we propose a novel network called Face Self-attention Network (FSN) to achieve high performance. Our method conducts extensive experiments on the standard WIDER FACE benchmark. The experimental results demonstrate that FSN can detect multi-scale faces accurately.

This work is supported by the National Natural Science Foundation of China (61873259, U20A20200,61821005), and the Youth Innovation Promotion Association of Chinese Academy of Sciences (2019203).

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Authors and Affiliations

  1. College of Information Engineering, Shenyang University of Technology, Shenyang, 110870, China

    Shunfu Hua & Wei Li

  2. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China

    Shunfu Hua, Huijie Fan, Naida Ding & Yandong Tang

  3. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China

    Shunfu Hua, Huijie Fan, Naida Ding & Yandong Tang

Authors
  1. Shunfu Hua
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  2. Huijie Fan
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  3. Naida Ding
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  4. Wei Li
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  5. Yandong Tang
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Corresponding author

Correspondence to Huijie Fan .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Honghai Liu

  2. Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

  3. Shenyang Institute of Automation, Shenyang, Liaoning, China

    Lianqing Liu

  4. Harbin Institute of Technology, Harbin, China

    Li Jiang

  5. Shanghai Jiao Tong University, Shanghai, China

    Guoying Gu

  6. Shenzhen Institutes of Advanced Technology, Shenzhen, China

    Xinyu Wu

  7. Harbin Institute of Technology, Shenzhen, China

    Weihong Ren

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Hua, S., Fan, H., Ding, N., Li, W., Tang, Y. (2022). A Self-attention Network for Face Detection Based on Unmanned Aerial Vehicles. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13456. Springer, Cham. https://doi.org/10.1007/978-3-031-13822-5_39

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  • DOI: https://doi.org/10.1007/978-3-031-13822-5_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13821-8

  • Online ISBN: 978-3-031-13822-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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