Journal of Applied Optics, Volume. 44, Issue 1, 86(2023)

Image segmentation method of surface defects for metal workpieces based on improved U-net

Yi WANG1...2, Xiaojie GONG1,*, and Hao SU13 |Show fewer author(s)
Author Affiliations
  • 1College of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, China
  • 2Tangshan Technology Innovation Center of Intellectualization of Metal Component Production Line, Tangshan 063210, China
  • 3Tangshan Key Laboratory of Semiconductor Integrated Circuits, Tangshan 063210, China
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    For the small-size defects of metal workpiece surface and the difficult segmentation of image defects due to non-uniform illumination, an improved U-net semantic segmentation network was proposed to achieve accurate image segmentation of surface defects for metal workpieces. Firstly, the convolutional block attention module (CBAM) was integrated into the U-net netwok to improve the significance of the defective targets in the image. Secondly, part of the traditional convolution in the network was replaced by depthwise over-parameterized convolution (DO-Conv) to increase the number of learnable parameters of the network. Then, the Leaky Relu function was used instead of the partial Relu function in the network to improve the feature extraction ability of the model for the negative intervals. Finally, the median filtering and non-uniform illumination compensation method were used for image preprocessing, so as to reduce the effect of non-uniform illumination on the surface defects of metal workpiece images. The results show that the improved network mean intersection over union, accuracy rate and Dice coefficient index reaches 0.833 5, 0.933 2 and 0.867 4, respectively. The improved network significantly improves the segmentation effect of surface defect images of metal workpieces.

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    Yi WANG, Xiaojie GONG, Hao SU. Image segmentation method of surface defects for metal workpieces based on improved U-net[J]. Journal of Applied Optics, 2023, 44(1): 86

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    Paper Information

    Category: Research Articles

    Received: Mar. 24, 2022

    Accepted: --

    Published Online: Feb. 22, 2023

    The Author Email: GONG Xiaojie (1692994031@qq.com)

    DOI:10.5768/JAO202344.0102004

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