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TBM Tunnel Surrounding Rock Debris Detection Based on Improved YOLO v8

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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

Real time detection of debris particle size is an important means to ensure the safe and efficient construction of TBM. In response to the problems of similar background, random and diverse contours, dense distribution, and overlapping accumulation of TBM debris, an improved YOLO v8 model for TBM tunnel surrounding rock debris detection is proposed. Using the preprocessing methods of ACE algorithm and CLAHE algorithm to improve image illumination intensity and contrast; Introducing deformable convolution to adapt to the irregular shape of debris; Add attention mechanism to the feature channel and selectively emphasize fragment features using global information to solve the problem of similar backgrounds; In the Prediction section, a dynamic non monotonic focusing mechanism is used to improve the quality of the anchor frame and further enhance the detection accuracy of debris recognition. Engineering validation was carried out based on the Dianzhong Water Diversion TBM project, and the results showed that the recognition rate of this method reached 95.7%, and the detection speed reached 43 FPS.

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Acknowledgments

This work was supported in part by the National Key Research and Development Program of China under Grant 2020YFB2007200.

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Authors and Affiliations

  1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China

    Lianhui Jia

  2. Railway Engineering Equipment Group Co., Ltd., No. 99, 6th Avenue National Economic & Technical Development Zone, Henan, 450016, Zhengzhou, China

    Lianhui Jia, Heng Wang, Yongliang Wen & Lijie Jiang

  3. Zhejiang University, Hangzhou, 310058, Zhejiang, China

    Lijie Jiang

Authors
  1. Lianhui Jia
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  2. Heng Wang
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  3. Yongliang Wen
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  4. Lijie Jiang
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Corresponding author

Correspondence to Lianhui Jia .

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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Jia, L., Wang, H., Wen, Y., Jiang, L. (2023). TBM Tunnel Surrounding Rock Debris Detection Based on Improved YOLO v8. 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_15

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  • DOI: https://doi.org/10.1007/978-981-99-6480-2_15

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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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