Laser & Optoelectronics Progress, Volume. 59, Issue 12, 1215010(2022)

Fringe Segmentation Algorithm Based on Improved U-Net

Wenwei Yan1,2,3,4, Shuai Chen1,2,4、*, Baoyan Mu1,2,4, and Liang Gao1,2,4
Author Affiliations
  • 1Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, Liaoning , China
  • 2Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, Liaoning , China
  • 3University of Chinese Academy of Sciences, Beijing 100049, China
  • 4Key Laboratory on Intelligent Detection and Equipment Technology of Liaoning Province, Shenyang 110179, Liaoning , China
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    To improve the accuracy of light stripe segmentation in the traditional vision measurement system based on line-structured light, an improved light stripe segmentation algorithm based on U-Net is proposed. The proposed algorithm uses the convolution pooling layer of VGG16 instead of that in the U-Net coding block, introduces the coordinate attention mechanism in the hop connection between U-Net coding and decoding layers, and connects the pyramid pooling module at the end of U-Net coding block. Additionally, it uses a combination of Dice function and cross entropy function as the loss function of the network, so as to solve the problem of imbalance of light stripe proportion. Based on the principle of line-structured light measurement, a workpiece size measurement system is designed. Experimental results show that the mean pixel accuracy (mpa) of the improved U-Net algorithm is 95.61% and mean intersection over union (mIoU) is 89.73%, which are higher than other comparison algorithms. The absolute error of workpiece measurement size is less than 0.1 mm, the relative error is less than 1%, and the repetition accuracy is less than 0.2%, meeting the inspection requirements of the workpiece.

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    Wenwei Yan, Shuai Chen, Baoyan Mu, Liang Gao. Fringe Segmentation Algorithm Based on Improved U-Net[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1215010

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    Paper Information

    Category: Machine Vision

    Received: Jul. 8, 2021

    Accepted: Aug. 17, 2021

    Published Online: May. 23, 2022

    The Author Email: Chen Shuai (chenshuai@sia.cn)

    DOI:10.3788/LOP202259.1215010

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