Automatic detection of micro image edges for micro accessories

LI JIANG; WU Wei-ren; ZHANG Zhi-jing; JIN Xin; JIE De-gang

doi:10.3788/OPE.20132101.0224

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Automatic detection of micro image edges for micro accessories

Article | 更新时间：2020-08-12

- Automatic detection of micro image edges for micro accessories
- Optics and Precision Engineering Vol. 21, Issue 1, Pages: 224-232(2013)
- 作者机构：
  
  北京理工大学机械与车辆工程学院2. 国防科工委月球探测工程中心
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20132101.0224
  CLC： TP391.4
- Received：26 June 2012，
  
  Revised：17 September 2012，
  
  Published Online：24 January 2013，
  
  Published：15 January 2013
- 稿件说明：
移动端阅览
姜黎吴伟仁张之敬金鑫节德刚. 微小型结构件显微图像边缘的自动识别[J]. 光学精密工程, 2013,21(1): 224-232

LI JIANG WU Wei-ren ZHANG Zhi-jing JIN Xin JIE De-gang. Automatic detection of micro image edges for micro accessories[J]. Editorial Office of Optics and Precision Engineering, 2013,21(1): 224-232
姜黎吴伟仁张之敬金鑫节德刚. 微小型结构件显微图像边缘的自动识别[J]. 光学精密工程, 2013,21(1): 224-232 DOI： 10.3788/OPE.20132101.0224.

LI JIANG WU Wei-ren ZHANG Zhi-jing JIN Xin JIE De-gang. Automatic detection of micro image edges for micro accessories[J]. Editorial Office of Optics and Precision Engineering, 2013,21(1): 224-232 DOI： 10.3788/OPE.20132101.0224.

摘要

在前一阶段的研究工作中，针对微小型结构件的显微图像边缘提取，作者提出了一种基于工艺匹配的显微图像边缘提取算法（Micro Processing Technology Matching based Micro Image Edge Detection

MPTM-MIED）。使用MPTM-MIED提取图像边缘之前，需要手工从零件显微图像中选取边缘过渡区域，这一操作将极大影响微小型零件的检测速度，从而使MPTM-MIED不能应用在实时自动检测中。针对MPTM-MIED的这一不足，本文利用BP神经网络技术重新设计并实现了MPTM-MIED，提出了一种新的自动提取显微图像边缘的方法（An Automated Micro Image Edge Detection Method，AMIED）。为了验证该方法的有效性，本文还利用AMIED对4种工艺微小型结构件显微图像的边缘进行了提取，并对线切割工艺零件的尺寸进行了测量。边缘提取的分析结果表明：AMIED提取出的显微图像边缘与MPTM-MIED提取出的基本一致；与常用的边缘检测算法相比，AMIED提取出的显微图像的边缘线形连接程度较好。测量尺寸的分析结果表明：MPTM-MIED和AMIED测量的尺寸基本相同，与Canny测量的相比，更接近万能工具显微镜测得的尺寸。

Abstract

In the previous research

a Micro Processing Technology Matching based Micro Image Edge Detection method (MPTM-MIED) was developed for detecting micro image edges of micro accessories. Before employing MPTM-MIED an edge transition zone has to be picked up manually from the micro image of micro accessories. This exerts a considerable influence on inspection speed and thus prevents MPTM-MIED from measuring sizes of micro accessories online and automatically. To overcome this shortcoming BP Neural Networks has been employed to design and implement a novel Automated Micro Image Edge Detection method (AMIED). From analysis results for micro accessories edge detections the following two conclusions are arrived at: the detected edges by AMIED and MPTM-MIED are almost the same; compared with some common used detection algorithms AMIED has the smallest edge-connectivity. From analysis results for measured sizes the following conclusions are drawn: the measured sizes by AMIED are almost equal to these measured ones by MPTM-MIED; compared with Canny the measured sizes by AMIED are closer to these measured ones by MPTM-MIED.

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references

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