Chip recognition and classification system based on improved Otsu algorithm

CHEN Zhi-wei; LI Shi-feng; LIU Feng

Chinese

您当前的位置：

首页 >

文章列表页 >

Chip recognition and classification system based on improved Otsu algorithm

更新时间：2026-09-04

- Chip recognition and classification system based on improved Otsu algorithm
- Vol. 38, Issue 4, Pages: 6-10(2021)
- 作者机构：
  
  1. 武汉大学动力与机械学院,湖北,武汉,430072
  2. 武汉大学工业科学研究院,湖北,武汉,430072
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2021
- 稿件说明：
移动端阅览
陈志伟, 李世峰, 刘锋. 基于改进Otsu算法的芯片识别分类系统[J]. 微电子学与计算机, 2021, 38(4): 6-10.

CHEN Zhi-wei, LI Shi-feng, LIU Feng. Chip recognition and classification system based on improved Otsu algorithm[J]. 2021, 38(4): 6-10.
陈志伟, 李世峰, 刘锋. 基于改进Otsu算法的芯片识别分类系统[J]. 微电子学与计算机, 2021, 38(4): 6-10. DOI：

CHEN Zhi-wei, LI Shi-feng, LIU Feng. Chip recognition and classification system based on improved Otsu algorithm[J]. 2021, 38(4): 6-10. DOI：

摘要

为满足用户端芯片识别、分类统计的需求，针对光照系统光不均匀性问题，提出一种基于改进Otsu算法的芯片识别分类系统.通过对全局灰度值判断的方法，能够自适应地选择阈值分割方式，以达到识别不同底色芯片盒的目的; 通过采取改进Otsu自适应阈值的方法，以消除光照不均匀性带来的影响; 通过使用芯片轮廓先验信息，以对最终提取的芯片轮廓进行修正，保证每一个芯片的完整性.实验结果表明，采取该算法能够有效对不同类型芯片盒内芯片的轮廓进行提取，实现了芯片的识别与分类.

Abstract

In order to meet the requirements of chip recognition and classification statistics

a chip recognition and classification method based on improved Otsu algorithm is proposed to solve the problem of light nonuniformity in illumination system. By judging the global gray value

the threshold segmentation method can be adaptively selected to achieve the purpose of identifying different background color chip boxes. The method of improving the adaptive threshold of Otsu was adopted to eliminate the influence of illumination unevenness. By using the prior information of chip contour

the final extracted chip contour can be modified to ensure the integrity of each chip. Experimental results show that the algorithm can effectively extract the contour of chips in different types of chip boxes

and achieve chip recognition and classification.

关键词

Keywords

references

NGUYEN V N, JENSSEN R, ROVERSO D. Automatic autonomous vision-based power line inspection: a review of current status and the potential role of deep learning[J]. International Journal of Electrical Power % Energy Systems, 2018, 99: 107-120. DOI: 10.1016/j.ijepes.2017.12.016.

DO Y, LEE S, KIM Y. Vision-based surface defect inspection of metal balls[J]. Measurement Science and Technology, 2011, 22(10): 107001. DOI: 10.1088/0957-0233/22/10/ 107001.

李如平, 徐珍玉, 吴房胜. 图像处理耦合机器视觉的芯片表面缺陷检测研究与应用[J]. 湖北民族学院学报(: 自然科学版), 2015, 33(2): 210-214. DOI: 10.13501/j.cnki.42-1569/n.2015.06.024. LI R P, XU Z Y, WU F S. Research and application on the chip surface defect inspection based on image processing and machine vision[J]. Journal of Hubei University for Nationalities(Natural Science Edition), 2015, 33(2): 210-214. DOI: 10.13501/j.cnki.42-1569/n.2015.06.024.

段均. 芯片图像网格自动定位方法研究[J]. 测控技术, 2015, 34(5): 129-132. DOI: 10.19708/j.ckjs.2015.05.036. DUAN J. Automatic positioning method for chip image grid[J]. Measurement % Control Technology, 2015, 34(5): 129-132. DOI: 10.19708/j.ckjs.2015.05.036.

刘庆民, 张蕾, 李雪. 基于改进Canny的芯片图像边缘检测算法[J]. 计算机工程与设计, 2016, 37(11): 3063-3067. DOI: 10.16208/j.issn1000-7024.2016.11.036. LIU Q M, ZHANG L, LI X. Chip image edge detection based on improved Canny algorithm[J]. Computer Engineering and Design, 2016, 37(11): 3063-3067. DOI: 10.16208/j.issn1000-7024.2016.11.036.

张和颖, 吴玉香, 胡跃明. 基于遗传神经网络的芯片图像分割[J]. 半导体技术, 2010, 35(11): 1091-1094. DOI: 10.3969/j.issn.1003-353x.2010.11.011. ZHANG H Y, WU Y X, HU Y M. Image segmentation based on genetic neural networks[J]. Semiconductor Technology, 2010, 35(11): 1091-1094. DOI: 10.3969/j.issn.1003-353x.2010.11.011.

郑雪芳, 林意, 袁琦睦. 基于图像处理的芯片尺寸测量系统研究[J]. 计算机与数字工程, 2013, 41(11): 1829-1831. DOI: 10.3969/j.issn.1672-9722.2013.11.036. ZHENG X F, LIN Y, YUAN Q M. Measurement system for semiconductor chips based on image processing[J]. Computer % Digital Engineering, 2013, 41(11): 1829-1831. DOI: 10.3969/j.issn1672-9722.2013.11.036.

周迪, 夏哲雷. 一种改进的Otsu阈值分割算法[J]. 中国计量大学学报, 2016, 27(3): 319-323. DOI: 10.3969/j.issn.2096-2835.2016.03.014. ZHOU D, XIA Z L. An improved Otsu threshold segmentation algorithm[J]. Journal of China University of Metrology, 2016, 27(3): 319-323. DOI: 10.3969/j.issn.2096-2835.2016.03.014.

OTSU N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62-66. DOI: 10.1109/TSMC.1979.4310076.

高岳, 杨帆, 齐昊姝, 等. 芯片键合设备图像识别模块的研究与设计[J]. 电子工业专用设备, 2018, 47(5): 9-12. DOI:10.3 96 9/j.issn.1004-4507.2018.05.003. GAO Y, YANG F, QI H S, et al. Research and design of image recognition module for die bonding equipment[J]. Equipment for Electronic Products Manufacturing, 2018, 47(5): 9-12. DOI:10.3 96 9/j.issn.1004-4507.2018.05.003.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A review of research on PCB surface defect detection based on machine vision

3D virtual perimeter monitoring method for substation digital violation based on machine vision

Research on the design of blockchain-based AI-generated content traceability framework

Research on assembly action recognition based on MSS-YOLO-MPHands fusion architecture

Monte carlo analysis of V-layout trench SiC neutron detector

Related Author

XU Yiqi

XIAO Jinqiu

WANG Yucheng

GU Yitao

ZHAO Honghua

WANG Chen

SUN Wei

ZHENG Bei

Related Institution

College of Electronics and Information Engineering, Suzhou University of Science and Technology

Intelligent Measurement and Control Engineering Technology Research Center

College of Physical Science and Technology, Suzhou University of Science and Technology

State Grid Wuhu Electric Power Supply Company

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰