Chinese Journal of Lasers, Volume. 50, Issue 16, 1602108(2023)

Laser Fringe Segmentation and Feature Points Location Method of Weld Image Based on Multi-Task Learning

Yigeng Huang1,2, Daqing Wang1、*, Man Jiang1, Haoyu Yin1, and Lifu Gao1,2
Author Affiliations
  • 1Institute of Intelligent Machines, Hefei Institute of Physical Science, Chinese Academy of Science, Hefei 230031, Anhui, China
  • 2University of Science and Technology of China, Hefei 230026, Anhui, China
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    Figures & Tables(15)
    Schematic of weld information measurement
    Projection model measurement (Oc-XcYcZc and uv are camera coordinate system and image coordinate system, respectively)
    Network structure. (a) Our network; (b)-(d) Detail/Seg Head, ARM, and FFM modules used in the model
    STDC module
    DSNT module
    Training loss of proposed model
    Detail label generation
    Laser stripe segmentation results. (a) Original images; (b) laser stripe label images; (c) weld seam features extracted by FCN-8s; (d) features extracted by our method with detailed information supervision; (e) features extracted by our method without detailed information supervision
    Location results of weld feature points by DSNT method under different noise interferences, where the green and blue “+” are left and right feature points and yellow “+” is intermediate feature point
    Comparison of feature point location results. (a1)-(a3) Extraction errors of left feature point, intermediate feature point, and right feature point in u-axis direction in weld image, respectively; (b1)-(b3) left feature point, intermediate feature point, and right feature point in v-axis direction in weld image, respectively
    Comparison of location errors of subtask correlation feature points. (a) In u-axis direction; (b) in v-axis direction
    Time consumption in processing for image sequence
    • Table 1. Feature point positioning branch structure

      View table

      Table 1. Feature point positioning branch structure

      StageOutputKsizeStridePaddingChannel quantity
      ConvX L160×7031164
      ConvX L260×7031132
      Conv2d HM60×701103
      DSNT3×2
    • Table 2. Comparison of laser stripe segmentation accuracy

      View table

      Table 2. Comparison of laser stripe segmentation accuracy

      ModelResolution /(pixel×pixel)MIOU /%FPS /(frame·s-1
      FCN-8s480×56089.0723
      BiSeNetV1480×56096.6742
      BiSeNetV2480×56093.95103
      STDC1-seg480×56099.1272
      U-Net480×56093.8516
      Ours480×56095.9787
      Ours-detail480×56098.8287
    • Table 3. Comparison of inference time of different networks

      View table

      Table 3. Comparison of inference time of different networks

      ModelTask

      Inference

      time /ms

      Laser fringe segmentationFeature point location
      FCN-8sYesNo43.4783
      BiSeNetV1YesNo23.6123
      BiSeNetV2YesNo9.6583
      ICNetYesNo48.2594
      OursYesYes11.4478
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    Yigeng Huang, Daqing Wang, Man Jiang, Haoyu Yin, Lifu Gao. Laser Fringe Segmentation and Feature Points Location Method of Weld Image Based on Multi-Task Learning[J]. Chinese Journal of Lasers, 2023, 50(16): 1602108

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    Paper Information

    Category: Laser Forming Manufacturing

    Received: Jul. 15, 2022

    Accepted: Sep. 13, 2022

    Published Online: Jul. 31, 2023

    The Author Email: Wang Daqing (dqwang@iim.ac.cn)

    DOI:10.3788/CJL221057

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