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Automatic detection of multi-crossing crack defects in multi-crystalline solar cells based on machine vision

机译：基于机器视觉的多晶硅电池多交叉裂纹缺陷的自动检测

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摘要

The detection of defects in solar cells based on machine vision has become the main direction of current development, but the graphical feature extraction of micro-cracks, especially cracks with complex shapes, still faces formidable challenges due to the difficulties associated with the complex background, non-uniform texture, and poor contrast between crack defects and background. In this paper, a novel detection scheme based on machine vision to detect multi-crossing cracks for multi-crystalline solar cells was proposed. First, faced with periodic noise, we improved the filter method in the frequency domain and eliminated the background interference of fingers by filtering out the periodic noise while retaining the integrity of the crack signal. To address the anisotropy of multi-crossing cracks, we designed a special grid-shaped, convolution kernel filter to accurately extract crack features at low contrast and in the presence of a complex textured background. Finally, to address the missing features from the central region of multi-crossing cracks, we designed a method based on the orientation information of mask pattern to implement feature reconstruction for the central region of the crack. The experimental results showed that, compared to other crack detection methods, the strategy designed herein exhibited a better detection performance and stronger robustness.
机译：基于机器视觉的太阳能电池缺陷的检测已成为电流发育的主要方向，但是由于与复杂背景相关的困难，微裂纹，特别是具有复杂形状的裂缝的图形特征提取仍然面临着强大的挑战裂缝缺陷与背景之间的非均匀纹理和差的对比度。本文提出了一种基于机器视觉的新型检测方案，以检测多结晶太阳能电池的多交叉裂缝。首先，面对定期噪声，我们通过在保持裂纹信号的完整性的同时通过过滤周期性噪声来改进滤波器方法并消除了手指的背景干扰。为了解决多交叉裂缝的各向异性，我们设计了一种特殊的网格形状，卷积核过滤器，以精确提取低对比度的裂纹特征，并且在存在复杂的纹理背景。最后，为了解决来自多交叉裂缝的中心区域的缺失特征，我们设计了一种基于掩模图案的方向信息的方法，以实现裂缝中心区域的特征重构。实验结果表明，与其他裂纹检测方法相比，这里设计的策略表现出更好的检测性能和更强的鲁棒性。

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  • 来源
    《Machine Vision and Applications》 |2021年第3期|60.1-60.14|共14页
  • 作者

    Yongzhong Fu; Xiaolong Ma; Hang Zhou;

  • 作者单位

    Engineering Institute of Advanced Manufacturing and Modern Equipment Technology Jiangsu University Zhenjiang 212013 China;

    Engineering Institute of Advanced Manufacturing and Modern Equipment Technology Jiangsu University Zhenjiang 212013 China;

    Engineering Institute of Advanced Manufacturing and Modern Equipment Technology Jiangsu University Zhenjiang 212013 China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Defect detection; Machine vision; Solar cells; Multi-crossing crack; Pattern recognition;

    机译：缺陷检测;机器视觉;太阳能电池;多交叉裂缝;模式识别;

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