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Improved Deep Learning-Based Efficientpose Algorithm for Egocentric Marker-Less Tool and Hand Pose Estimation in Manual Assembly

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Advanced Intelligent Computing Technology and Applications (ICIC 2023)

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Abstract

Different manual assembly orientations have a significant impact on assembly accuracy. The success or confidence of posture estimation depends on the accurate six degree-of-freedom (6DoF) position and orientation (pose) estimation of the tracked objects. In this paper, we present an improved Efficient Pose algorithm, which is a single-shot learning-based approach to hand and object pose estimation. Based on the original Efficient Pose algorithm, we added a subnetwork for hand prediction, replaced some MBConv modules with Fused-MBConv modules, modified the number of network layers, and used different training strategies. Experimental results show that on the public dataset for monocular red-green-blue (RGB) 6DoF marker-less hand and surgical instrument pose tracking, it improves performance and shortens training time compared to other methods.

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Acknowledgement

This work was supported by the National Natural Science Foundation of China (Nos. 62072002 and 62172004), and Special Fund for Anhui Agriculture Research System.

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Authors and Affiliations

  1. National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Internet and Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China

    Zihan Niu, Yi Xia, Jun Zhang & Peng Chen

  2. School of Electrical and Information Engineering, Anhui University of Technology, Maanshan, 243032, Anhui, China

    Bing Wang

Authors
  1. Zihan Niu
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  2. Yi Xia
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  3. Jun Zhang
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  4. Bing Wang
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  5. Peng Chen
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Corresponding author

Correspondence to Peng Chen .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Eastern Institute of Technology, Zhejiang, China

    De-Shuang Huang

  2. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  3. Zhengzhou University of Light Industry, Zhengzhou, China

    Baohua Jin

  4. Zhong Yuan University of Technology, Zhengzhou, China

    Boyang Qu

  5. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  6. Department of Computer Science, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Niu, Z., Xia, Y., Zhang, J., Wang, B., Chen, P. (2023). Improved Deep Learning-Based Efficientpose Algorithm for Egocentric Marker-Less Tool and Hand Pose Estimation in Manual Assembly. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science(), vol 14090. Springer, Singapore. https://doi.org/10.1007/978-981-99-4761-4_25

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  • DOI: https://doi.org/10.1007/978-981-99-4761-4_25

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  • Print ISBN: 978-981-99-4760-7

  • Online ISBN: 978-981-99-4761-4

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