| 李德健,李绍丽,苑玮琦,曲津助.基于自适应方向模板搜索的 FPC 走线薄浆检测[J].电子测量与仪器学报,2022,36(7):177-188 |
| 基于自适应方向模板搜索的 FPC 走线薄浆检测 |
| FPC trace paste attenuation detection based on adaptive directional template |
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| DOI： |
| 中文关键词: 自适应方向模板 灰度差分 FPC 走线 薄浆 |
| 英文关键词:adaptive directional template gray difference FPC trace paste attenuation |
| 基金项目:辽宁省教育厅自然科学基金（LQGD2020015）项目资助 |
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| 摘要点击次数: 688 |
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| 中文摘要: |
| 本文薄浆是柔性印刷电路板(FPC)导电涂层的一种缺陷,为避免其对电路性能产生不良影响而造成经济损失,将薄浆
位置检测出来进行修补或直接剔除板材均是合理的处理方式。 然而由于薄浆缺陷尺寸不定、光照不均致使图像灰度分布不均
匀、走线区域与背景纵横交错等问题,使得走线上薄浆的检测具有一定难度。 针对该问题,在对薄浆纹理及 FPC 走线形态特征
进行详细分析的基础上,提出了一种基于自适应方向模板(ADT)和灰度差分法的检测方案。 首先,使用定义的 ADT 模板按照
骨架跟踪流程对预处理所得图像的走线区域进行遍历,分别搜索相对于该 ADT 模板尺寸而定义的大、小尺度薄浆纹理的端部
和全部候选像素,从而将预处理图像分割成若干小区域;然后,根据各小区域的能量倾向值与预设阈值的数值关系对其中的边
缘噪声区域加以排除;最后,采用灰度差分法对上述两个步骤处理所得结果中的非缺陷区域进行再次筛查,从中提取出大尺度
薄浆纹理的中间区域,至此实现全部薄浆纹理的检测。 在自建图库 SUT-F2 上进行了测试,结果显示本文方法对薄浆缺陷检测
的等误率仅为 3. 92%,相对于其他典型纹理特征提取和薄浆检测方法其等误率至少降低了 5. 28%,表明了本文方法的高效性,
具有实际应用价值。 |
| 英文摘要: |
| The paste attenuation mentioned in this paper is a conductive coating flaw on flexible printed circuit board. For avoiding
harmful effect caused by paste attenuation on FPC trace, which would cause economical losses, it is a reasonable way to locate the
position of paste attenuation for the later repair or get rid of the unqualified board directly. However, the size of paste attenuation flaw is
uncertain. And the non-uniform illumination results in the non-uniform of image grayscale. Also trace region crisscrosses with the
background. The above reasons increase the difficulty of trace paste attenuation detection. Aiming at this problem, a detection scheme is
proposed which made up of adaptive directional template and gray difference, based on the detailed analysis of paste attenuation texture
and trace morphological feature. First of all, the trace region after preprocessing is traversed with ADT template according to skeleton
tracking process, then the large paste attenuation regions, diminutive paste attenuation regions and the whole candidate pixels relative to
the ADT template size, thus, the preprocessed image is segmented into several diminutive regions. Secondly, the noise regions which are
not flaws are eliminated utilizing the numerical relationship of diminutive regions energy tendency value and default threshold. At last,
the non-deficit regions gained after the above two steps processing are screened again via method of gray difference, then the middle
region of large paste attenuation texture is extracted, thus, the whole paste attenuation texture detection is completed. Results show that
the paste attenuation flaw detection EER of suggested method is 3. 93 percent on the testing of self-built image storage SUT-F2, which
decreases at least 5. 28 percent compared to the other typical methods of textural features extraction and paste attenuation flaw detection.
The above facts prove the high efficiency and actual value of suggested method. |
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