基于网格的多目标模板匹配晶粒位置检测方法

周书辰,陈晓荣,王子旋

计量学报 ›› 2024, Vol. 45 ›› Issue (5) : 639-645.

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PDF(839 KB)
计量学报 ›› 2024, Vol. 45 ›› Issue (5) : 639-645. DOI: 10.3969/j.issn.1000-1158.2024.05.05
光学计量

基于网格的多目标模板匹配晶粒位置检测方法

  • 周书辰,陈晓荣,王子旋
作者信息 +

Grid-based Multi-targets Template Matching Die Position Detection Method

  • ZHOU Shuchen,CHEN Xiaorong,WANG Zixuan
Author information +
文章历史 +

摘要

针对传统方法中晶粒位置检测的耗时长、精度低等局限性,提出一种基于网格的多目标模板匹配晶粒位置检测方法。通过改进传统的模板匹配方法,结合非极大值抑制算法,将芯片晶粒的检测速度和精度提高。实验结果表明:该算法在单一同种晶粒的算法识别率能够达到97%以上,单张图像耗时<200ms,能够克服明暗不同造成的检测困难,达到技术指标要求。

Abstract

To address the limitations of traditional methods in terms of long detection time and low accuracy, a multi-objective template matching algorithm detecting position of die based on grid is proposed. By improving the traditional template matching method and combining with non-maximum suppression algorithm, the detection speed and accuracy of chip dice are improved. Experimental results show that the proposed algorithm has a recognition rate of over 97% for a single same die, and the single image processing time does not exceed 200ms. It can overcome the detection difficulties caused by different brightness and meet the technical specifications.

关键词

光电检测;晶粒位置检测;机器视觉;非极大值抑制;网格 / 多目标模板匹配

Key words

photoelectric detection / die position detection / machine vision / non-maximum suppression / grid / multi-target template matching

引用本文

导出引用
周书辰,陈晓荣,王子旋. 基于网格的多目标模板匹配晶粒位置检测方法[J]. 计量学报. 2024, 45(5): 639-645 https://doi.org/10.3969/j.issn.1000-1158.2024.05.05
ZHOU Shuchen,CHEN Xiaorong,WANG Zixuan. Grid-based Multi-targets Template Matching Die Position Detection Method[J]. Acta Metrologica Sinica. 2024, 45(5): 639-645 https://doi.org/10.3969/j.issn.1000-1158.2024.05.05
中图分类号: TB96    TB973   

参考文献

[1]于志斌, 胡泓. 基于YOLO算法与机器视觉的晶粒片表面缺陷检测研究[J]. 新型工业化, 2021, 11(12): 114-117.
YU Z B, HU H. Research on Surface Defect Detection of Grain Chips Based on YOLO Algorithm and Machine Vision[J]. The Journal of New Industrialization, 2021, 11(12): 114-117.
[2]陈治杉, 刘本永. 基于机器视觉的晶粒表面缺陷检测[J]. 贵州大学学报(自然科学版), 2019, 36(4): 68-73.
CHEN Z S, LIU B Y. Die surface defect detection based on machine vision[J]. Journal of Guizhou University, 2019, 36(4): 68-73.
[3]SAQLAIN M, ABBAS Q, LEE J Y. A deep convolutional neural network for wafer defect identification on an imbalanced dataset in semiconductor manufacturing processes[J]. IEEE Transactions on Semiconductor Manufacturing, 2020, 33(3): 436-444.
[4]周作梅, 李俊杰. 基于高斯滤波的低照度图像信息增强方法[J]. 信息与电脑(理论版), 2022, 34(17): 202-204.
ZHOU Z M, LI J J. A Low Illumination Image Information Enhancement Method Based on Gaussian Filter[J]. China Computer & Communication, 2022, 34(17): 202-204.
[5]周美丽, 白宗文. 基于Matlab的高斯模糊图像去噪方法研究[J]. 电子设计工程, 2014, 22(19): 167-168,172.
ZHOU M L, BAI Z W. Research on Gaussian Blur Image Denoising Method Based on Matlab[J]. International Electronic Elements, 2014, 22(19): 167-168,172.
[6]姒绍辉, 胡伏原, 顾亚军, 等. 一种基于不规则区域的高斯滤波去噪算法[J]. 计算机科学, 2014, 41(11): 313-316.
SI S H, HU F Y, GU Y J, et al. A Gaussian Filter Denoising Algorithm Based on Irregular Regions[J]. Computer Science, 2014, 41(11): 313-316.
[7]LIN H, DU P, ZHAO W, et al. Image registration based on corner detection and affine transformation[C]//2010 3rd International Congress on Image and Signal Processing. Yantai, China. 2010.
[8]DONG P, GALASANOS N P. Affine transformation resistant watermarking based on image normalization[C]//Proceedings International Conference on Image Processing. Rochester,  USA. 2002.
[9]曾文锋, 李树山, 王江安. 基于仿射变换模型的图像配准中的平移、旋转和缩放[J]. 红外与激光工程, 2001(1): 18-20,17.
ZENG W F, LI S S, WANG J A. Translation, Rotation, and Scaling in Image Registration Based on Affine Transformation Model[J]. Infrared and Laser Engineering, 2001(1): 18-20,17.
[10]WEISSTEIN E W. Affine transformation [EB/OL]. https: //mathworld. wolfram. com/, 2004.
[11]贾迪, 朱宁丹, 杨宁华, 等. 图像匹配方法研究综述[J]. 中国图象图形学报, 2019, 24(5): 677-699.
JIA D, ZHU N D, YANG N H, et al. Overview of Research on Image Matching Methods[J]. Journal of Image and Graphics , 2019, 24(5): 677-699.
[12]JIANG S P, XIANG W, LIU Y P, et al. Template matching using multi-feature co-occurrence matrix[J]. Optics and Precision Engineering, 2021, 29(6): 1459-1467.
[13]CHEN Z Y, LIU T, XIE J B, et al. Joint template matching algorithm for associated multi-object detection[J]. KSII Transactions on Internet and Information Systems, 2012, 6(1): 394.
[14]BRUNELLI R. Template matching techniques in computer vision: theory and practice[M]. New York:John Wiley & Sons, 2009.
[15]HASHEMI N S, AGHDAM R B, GHIASI A S B, et al. Template Matching Advances and Applications in Image Analysis[J]. American Scientific Research Journal for Engineering,Technology and Sciences (ASRJETS), 2016, 26(3): 91-108.
[16]WANG L T, LIU Q R. Discriminant distance template matching for image recognition[J]. Machine Vision and Applications, 2022, 33(6): 91.
[17]HAN Y. Reliable Template Matching for Image Detection in Vision Sensor Systems[J]. Sensors, 2021, 21(24): 8176.
[18]韩硕, 陈晓荣, 张彩霞, 等. 基于改进模板匹配算法的物料计数方法研究[J]. 计量学报, 2022, 43(7): 863-868.
HAN S, CHEN X R, ZHANG C X, et al. Research on Material Counting Method Based on Improved Template Matching Algorithm[J]. Acta Metrologica Sinica, 2022, 43(7): 863-868.
[19]侯志强, 刘晓义, 余旺盛, 等. 使用GIoU改进非极大值抑制的目标检测算法[J]. 电子学报, 2021, 49(4): 696-705.
HOU Z Q, LIU X Y, YU W S, et al. Using GIoU to Improve Non Maximum Suppression Target Detection Algorithm[J]. Chinese Journal of Electronics, 2021, 49(4): 696-705.
[20]BODLA N, SINGH B, CHELLAPPA R, et al. Soft-NMS-improving object detection with one line of code[C]//Proceedings of the IEEE international conference on computer vision. Venice, Italy, 2017.
[21]LIU S, HUANG D, WANG Y. Adaptive nms: Refining pedestrian detection in a crowd[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. Long Beach, Los Angeles, USA, 2019.
[22]金柯, 陈晓荣. 基于机器视觉的光纤端面几何参数测量研究[J]. 计量学报, 2023, 44(2): 165-170.
JIN K, CHEN X R. Research on Measurement of Geometric Parameters of Optical Fiber Endface Based on Machine Vision[J]. Acta Metrologica Sinica, 2023, 44(2): 165-170.
[23]陈宗元, 张磊磊, 赵宁宁, 等. 混合颗粒系重叠图像分割与分类方法研究[J]. 计量学报, 2022, 43(6): 745-752.
CHEN Z Y, ZHANG L L, ZHAO N N, et al. Research on Segmentation and Classification Methods of Mixed Overlapped Particle Images[J]. Acta Metrologica Sinica, 2022, 43(6): 745-752.

基金

国家自然科学基金(52175513)

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