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Exposure-fusion-based dot-grid image acquisition and recognition for sheet metal strain analysis

机译：基于曝光融合的点网图像采集与钣金应变分析的识别

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摘要

Dot-grid images are usually captured for grid strain analysis during sheet metal forming. Due to the strong reflective characteristic of the metallic surfaces, the recorded dot-grid images often have poor quality, low positioning accuracy, and low recognition rate. Therefore, an exposure-fusion-based dot-grid image acquisition and recognition approach is proposed. First, multiple dot-grid images are captured at different exposure levels. Subsequently, the recorded multi-exposure dot-grid images are fused into a new high-quality dot-grid image based on exposure fusion technology. Finally, a dot-grid image recognition procedure is developed to detect the dot-grids in the new dot-grid image. Both synthetic and real dot-grid images were tested to verify the performance of the novel approach. When synthetic dot-grid images were tested, the maximum positioning error was up to 6.044 pixels if they were recognized in the traditional way, whereas the maximum positioning error was reduced to 0.132 pixels if the novel approach was adopted. When real dot-grid images were tested, the lowest recognition rate is only 50.52% if they were recognized in the traditional way. Nevertheless, the recognition rate can reach about 91% if the novel approach was employed. These experimental results show the superiorities of the novel approach. (C) 2017 Optical Society of America
机译：通常在金属板形成期间捕获点网格图像进行网格应变分析。由于金属表面的强反射特性，所记录的点网格图像通常具有差的质量，低定位精度和低识别率。因此，提出了一种曝光融合的点网图像采集和识别方法。首先，在不同的曝光水平下捕获多个点网格图像。随后，将记录的多曝光点网格图像融合到基于曝光融合技术的新型高质量点网格图像中。最后，开发了点网格图像识别过程以检测新的点网格图像中的点网格。测试了合成和真实点网格图像，以验证新方法的性能。当测试合成点网格图像时，如果采用新方法，则最大定位误差高达6.044像素，而最大定位误差减少到0.132像素。当测试真实点网格图像时，如果以传统方式认可，则最低识别率仅为50.52％。尽管如此，如果采用新方法，识别率可以达到约91％。这些实验结果表明了新方法的优势。 （c）2017年光学学会

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  • 来源
    《Applied optics》 |2017年第35期|共10页
  • 作者

    Shi Bao-Quan; Zhang Li-Kun; Yao Chen-Song; Du Shu-Xing; Ye JunJie;

  • 作者单位

    Xidian Univ Minist Educ Key Lab Elect Equipment Struct Design Xian 710071 Shaanxi Peoples R China;

    Xidian Univ Minist Educ Key Lab Elect Equipment Struct Design Xian 710071 Shaanxi Peoples R China;

    Xidian Univ Minist Educ Key Lab Elect Equipment Struct Design Xian 710071 Shaanxi Peoples R China;

    Xidian Univ Minist Educ Key Lab Elect Equipment Struct Design Xian 710071 Shaanxi Peoples R China;

    Xidian Univ Minist Educ Key Lab Elect Equipment Struct Design Xian 710071 Shaanxi Peoples R China;

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  • 正文语种 eng
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