张思祥,胡雪迎,竭霞,李思鸣,王哲,赵子豪,周围.单体热电池装配缺陷的图像检测方法研究[J].电子测量与仪器学报,2019,33(2):132-139
单体热电池装配缺陷的图像检测方法研究
Research on image detection method for assembly failure of monomer thermal battery
  
DOI:
中文关键词:  单体热电池  改进灰度共生矩阵  HU不变矩  模板匹配  CART决策树分类器
英文关键词:monomer thermal battery  improved gray level co occurrence matrix  HU invariant moment  template matching  classification and regression tree(CART) decision tree
基金项目:“十三五”装备预研共用技术(41421070102)资助项目
作者单位
张思祥 1.河北工业大学机械工程学院 
胡雪迎 1.河北工业大学机械工程学院 
竭霞 1.河北工业大学机械工程学院 
李思鸣 1.河北工业大学机械工程学院 
王哲 1.河北工业大学机械工程学院 
赵子豪 1.河北工业大学机械工程学院 
周围 1.河北工业大学机械工程学院 
AuthorInstitution
Zhang Sixiang 1.College of Mechanical Engineering, Hebei University of Technology 
Hu Xueying 1.College of Mechanical Engineering, Hebei University of Technology 
Jie Xia 1.College of Mechanical Engineering, Hebei University of Technology 
Li Siming 1.College of Mechanical Engineering, Hebei University of Technology 
Wang Zhe 1.College of Mechanical Engineering, Hebei University of Technology 
Zhao Zihao 1.College of Mechanical Engineering, Hebei University of Technology 
Zhou Wei 1.College of Mechanical Engineering, Hebei University of Technology 
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中文摘要:
      针对目前国内外热电池内部装配缺陷检测准确度不高的问题,研究一种基于图像检测热电池内部的单体热电池缺陷的检测方法。其中分析了单体热电池整体倒装、单体热电池装配次序、单体热电池漏装集流片3种常见的缺陷的特征, 利用改进的灰度共生矩阵、HU 不变矩和模板匹配三种算法对单体热电池进行缺陷分析。最后利用分类回归树(CART)进行检测,提出一种按权重分配参数的检测方法,实验结果表明,这种方法准确度达到 975%满足检测要求,为热电池缺陷检测提供了有效途径。
英文摘要:
      A method for detecting defects of monomer thermal battery inside the thermal battery is proposed in this paper, which aimed at the problem of the low accuracy of internal assembly fault detection at home and abroad. This detection includes three defects, the overall flip chip of the monomer thermal battery, the assembly sequence of the monomer thermal battery, and the leakage of the monomer thermal battery part are analyzed. Using the improved gray level co occurrence matrix, HU invariant moment, template matching to analyze the defects of monomer thermal batteries. Finally, proposing a detection method based on weight distribution parameters, which is using CART (Classification and Regression Tree) decision tree for detection. The experimental results show that the accuracy of this method reaches 975% and meets the testing requirements, which provides an effective way for thermal battery defect detection.
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