Infrared and Laser Engineering, Volume. 51, Issue 4, 20210183(2022)

Automatic assembly positioning method of shield tunnel segments based on deep learning vision and laser assistance

Zhiyang Wu1,2,3, Shuang Wang1,2,3、*, Tiegen Liu1,2,3, and Dangpeng Jin4
Author Affiliations
  • 1School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
  • 2Key Laboratory of Optoelectronics Information Technology, Ministry of Education, Tianjin University, Tianjin 300072, China
  • 3Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing, Tianjin University, Tianjin 300072, China
  • 4Tian He Mechanical Equipment Manufacturing Co. Ltd, Changshu 215500, China
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    The positioning of tunnel segments is the key to realize the automatic assembly of shield segments. This paper proposed a method for automatic assembly and positioning of shield segments based on the combination of deep learning vision and laser assistance. The plane pose and depth pose information of the segments to be assembled were obtained by vision system and laser ranging system, respectively. The vision system based on the specially designed two-stage convolutional neural network could effectively extract the contour features of the segment surface positioning marks, and the extraction accuracy and recognition rate were significantly improved compared with existing algorithms. Experiments show that the proposed automatic assembly positioning method of shield segment can meet the requirements of automatic assembly and positioning of shield segment.

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    Zhiyang Wu, Shuang Wang, Tiegen Liu, Dangpeng Jin. Automatic assembly positioning method of shield tunnel segments based on deep learning vision and laser assistance[J]. Infrared and Laser Engineering, 2022, 51(4): 20210183

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    Paper Information

    Category: Lasers & Laser optics

    Received: Mar. 17, 2021

    Accepted: --

    Published Online: May. 18, 2022

    The Author Email: Wang Shuang (shuangwang@tju.edu.cn)

    DOI:10.3788/IRLA20210183

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