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Object Detection Networks and Mixed Reality for Cable Harnesses Identification in Assembly Environment

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Intelligent Computing Methodologies (ICIC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13395))

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Abstract

This paper proposes a smart cable assembly assistance system which composed of MR (mixed reality) cable assembly guidance sub-system and cable type inspection sub-system. In the cable type inspection sub-system, a deep object detection algorithm called Cable-YOLO is developed. The proposed algorithm integrates the multiple scale features and anchor-free mechanism. At the same time, a visual guidance sub-system is deployed in the MR device of Hololens to aid the cable assembly process. In order to evaluate the Cable-YOLO algorithm, a dataset containing 3 types of aviation cables is collected. The experimental results show that Cable-YOLO can recognize the cable type and locations, which achieves the best performance in detection accuracy and inference time comparing other four baseline algorithms. The visual guidance subsystem can assist the assembly worker who has a professional experience to install the cable at the corresponding location with the cable type detection results. The system can provide quick assembly guidance for the worker in a real environment.

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Authors and Affiliations

  1. Department of Engineering and Technology Center, No.38 Research Institute of CETC, Hefei, 230088, China

    Yixiong Wei, Hongqi Zhang, Hongqiao Zhou & Qianhao Wu

  2. Anhui Technical Standard Innovation Base (Intelligent Design and Manufacturing, Intelligence Institute, Civil-military Integration), Hefei, 230088, China

    Yixiong Wei, Hongqi Zhang, Hongqiao Zhou & Qianhao Wu

  3. National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, School of Internet, Anhui University, Hefei, 230601, China

    Zihan Niu

Authors
  1. Yixiong Wei
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  2. Hongqi Zhang
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  3. Hongqiao Zhou
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  4. Qianhao Wu
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  5. Zihan Niu
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Corresponding author

Correspondence to Qianhao Wu .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

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

    Kang-Hyun Jo

  3. Xi’an Polytechnic University, Xi’an, China

    Junfeng Jing

  4. The University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  5. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  6. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Wei, Y., Zhang, H., Zhou, H., Wu, Q., Niu, Z. (2022). Object Detection Networks and Mixed Reality for Cable Harnesses Identification in Assembly Environment. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2022. Lecture Notes in Computer Science(), vol 13395. Springer, Cham. https://doi.org/10.1007/978-3-031-13832-4_28

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  • DOI: https://doi.org/10.1007/978-3-031-13832-4_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13831-7

  • Online ISBN: 978-3-031-13832-4

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