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Real-Time Detection of Surface Floating Garbage Based on Improved YOLOv7

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Intelligent Robotics and Applications (ICIRA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14272))

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Abstract

Cleaning up surface floating garbage is of great significance to improve the ecological environment of the waters, of which the detection of surface floating garbage is a crucial step. It is difficult to distinguish the background and garbage and solve the problem of small targets of the existing target detection methods. Therefore, an improved YOLOv7 network is proposed to achieve efficient and high-precision detection of surface floating garbage. The newly proposed DS method is applied to original YOLOv7 network for multi-scale feature fusion. Meanwhile, CBAM attention mechanism module is applied to network backbone to improve detection precision. The public data set FLOW is used to validate the method in this paper. The simulation test results show that the proposed method significantly improves the detection precision without reducing too much detection speed, which proves the effectiveness of the proposed method. Finally, the proposed method is deployed to an NVIDIA Jetson device and applied to real-time surface floating garbage detection, which proves its practicability.

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Acknowledgments

This work was supported by the Liaoning Province Science and technology Fund Project (Grant No. 2021-MS-035) and the Research Fund of State Key Laboratory of Robotics (Grant No. 2020-Z04, No. 2021-Z11L02).

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Authors and Affiliations

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

    Liang Li, Yiping Li, Zhibin Jiang & Hailin Wang

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

    Liang Li, Yiping Li, Zhibin Jiang & Hailin Wang

  3. Key Laboratory of Marine Robotics, Shenyang, 110169, Liaoning, China

    Liang Li, Yiping Li & Hailin Wang

  4. University of Chinese Academy of Sciences, Beijing, 100049, China

    Liang Li & Hailin Wang

Authors
  1. Liang Li
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  2. Yiping Li
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  3. Zhibin Jiang
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  4. Hailin Wang
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Corresponding author

Correspondence to Liang Li .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Huayong Yang

  2. Harbin Institute of Technology, Shenzhen, China

    Honghai Liu

  3. Zhejiang University, Hangzhou, China

    Jun Zou

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

    Zhouping Yin

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

    Lianqing Liu

  6. Zhejiang University, Hangzhou, China

    Geng Yang

  7. Zhejiang University, Hangzhou, China

    Xiaoping Ouyang

  8. Harbin Institute of Technology, Shenzhen, China

    Zhiyong Wang

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Li, L., Li, Y., Jiang, Z., Wang, H. (2023). Real-Time Detection of Surface Floating Garbage Based on Improved YOLOv7. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14272. Springer, Singapore. https://doi.org/10.1007/978-981-99-6480-2_47

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  • DOI: https://doi.org/10.1007/978-981-99-6480-2_47

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-6479-6

  • Online ISBN: 978-981-99-6480-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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