基于图像检测的双绞线绕距测量方法

doi:10.11772/j.issn.1001-9081.2014.10.3014

计算机应用 ›› 2014, Vol. 34 ›› Issue (10): 3014-3019.DOI: 10.11772/j.issn.1001-9081.2014.10.3014

基于图像检测的双绞线绕距测量方法

王刚,石守东,林宜丙

宁波大学信息科学与工程学院，浙江宁波 315211

收稿日期:2014-04-28 修回日期:2014-06-18 发布日期:2014-10-30 出版日期:2014-10-01
通讯作者: 王刚
作者简介:王刚(1989-)，男，浙江绍兴人，硕士研究生,主要研究方向：图像处理;石守东(1964-), 男,浙江宁海人，副教授，博士,主要研究方向：机器视觉、图像处理；林宜丙(1991-)，男，福建宁德人，硕士研究生,主要研究方向：图像处理。
基金资助:
“信息与通信工程”浙江省重中之重学科开放基金资助项目

Pitch measurement methed of twisted-pair wire based on image detection

WANG Gang,SHI Shoudong,LIN Yibing

College of Information Science and Engineering, Ningbo University, Ningbo Zhejiang 315211, China

Received:2014-04-28 Revised:2014-06-18 Online:2014-10-30 Published:2014-10-01
Contact: WANG Gang
Supported by:
Information and communication engineering priority discipline open fund in zhejiang province

摘要/Abstract

摘要：

针对双绞线绕距测量，提出了一种图像检测框架。此框架通过图像分割、修复、细化、拟合以及比例尺的设置，可实时计算出双绞线绕距值。在此框架下，针对传统二维最大类间方差法——Otsu运行时间较长的问题，提出了一种新的基于区域斜分的快速算法。快速算法通过对二维直方图区域重新划分，结合快速查找表以及递推算法，大大减少了分割时间。针对图像缺失的问题，采用了基于边缘检测的算法对其特定区域进行填充修复，并对修复后的图像进行细化。通过最小二乘法，拟合细化图像中的单像素点，得到拟合曲线。通过计算拟合曲线交点间的距离可得双绞线绕距的图像距离。最后将图像距离按比例尺转换为绕距的测量值。实验结果表明，基于区域斜分的快速算法其分割时间约为传统算法的0.22%，且两种算法的分割效果基本一致。将图像检测方法测得的绕距值与其真实值进行比较，结果表明测量值与真实值的绝对误差为0.48%。通过使用图像检测方法测量双绞线绕距，可以准确测得绕距值，提高绕距测量的效率。

Abstract:

To measure the pitch of twisted-pair wires, a kind of image detection framework was put forward. With image segmentation, image restoration, image thinning, curve fitting and scale setting, the pitch of twisted-pair wires was calculated in real time. In combination with this framework, to deal with the problem that the traditional two-dimensional maximum between-cluster variance algorithm (Otsu) runs too slow, a new fast algorithm based on regional diagonal points was proposed. With redefining two-dimensional histogram area, using the quick lookup table and recursion method, it reduced running time drastically. To solve the problem of image missing, an edge detection algorithm was adopted. After repairing, the image thinning operation was acted on the image. The least square method was used to fit the single pixel point of thinning image, then fitting curve was acquired. It could acquire the pitch of twisted-pair wires in the image by calculating the distance between the fitting curve intersections. Finally the distance in image was converted to an observed value by the scale. The experimental results show that the segmentation time of fast algorithm is about 0.22% of traditional algorithm. And two segmentation results of algorithms are identical. With the pitch from the image detection method comparing with its real value, results show that the absolute errors between both of them are 0.48%. Through the image detection method, the pitch is measured accurately and the efficiency of twisted-pair pitch measurement is improved.

中图分类号:

TP391.41

王刚石守东林宜丙. 基于图像检测的双绞线绕距测量方法[J]. 计算机应用, 2014, 34(10): 3014-3019.

WANG Gang SHI Shoudong LIN Yibing. Pitch measurement methed of twisted-pair wire based on image detection[J]. Journal of Computer Applications, 2014, 34(10): 3014-3019.

参考文献

［1］LIU S. Research and application on the key technologies of industrial image detecting system ［D］. Shanghai: Shanghai Jiao Tong University, 2007.(刘圣.工业图像检测系统关键技术的研究及应用［D］.上海:上海交通大学,2007. )
［2］SHI B, WANG L, CAO J. On-line image detection method for matching quality of three-dimensional parts ［J］. Chinese Journal of Scientific Instrument, 2009,30(10):2039-2044.(史博,汪霖,曹建福.立体零件加工质量的在线图像检测方法［J］.仪器仪表学报,2009,30(10):2039-2044.)［3］LI W, ZHU P. Image detection algorithm research for asphalt pavement crack ［J］. Computer Engineering and Applications, 2012,48(19):163-166.(黎蔚,朱平哲.沥青路面裂缝图像检测算法研究［J］.计算机工程与应用,2012,48(19):163-166.)
［4］LU P, LI Y, WANG Z, et al.Fault-image detection algorithm based on visual perception ［J］. Chinese Journal of Scientific Instrument, 2010,31(9):1997-2002.(逯鹏,李永强,王治忠,等.基于视觉感知的故障图像检测算法［J］.仪器仪表学报,2010,31(9):1997-2002.)
［5］XU X, DING S, SHI Z, et al.New theories and methods of image segmentation ［J］. Acta Electronica Sinica, 2010,38(Z1):76-82.(许新征,丁世飞,史忠植,等.图像分割的新理论和新方法［J］.电子学报,2010,38(Z1):76-82.)
［6］LEI B, LAN R, FAN J. Fast algorithm for maximum fuzzy entropy thresholding method ［J］. Pattern Recognition and Artificial Intelligence, 2010,23(6):867-873.(雷博,兰蓉,范九伦.最大模糊熵阈值法的快速算法［J］.模式识别与人工智能,2010,23(6):867-873.)
［7］OTSU N. A threshold selection method from gray-level histogram ［J］. IEEE Transactions on System Man and Cybernetics, 1979,9(1):62-66.
［8］ZHAO Y, YANG Y, WANG K. Iterative multi-level image segmentation based on fuzzy set theory ［J］. Journal of Optoelectronics·Laser, 2009,20(10):1403-1409.(赵于前,杨元,王琨.基于模糊集理论的迭代多值化图像分割［J］.光电子·激光,2009,20(10):1403-1409.)
［9］WU C, TIAN X, TAN T. Modification of two-dimensional entropic threshold method and its fast iterative algorithm ［J］. Pattern Recognition and Artificial Intelligence, 2010,23(1):127-136.(吴成茂,田小平,谭铁牛.二维熵阈值法的修改及其快速迭代算法［J］.模式识别与人工智能,2010,23(1):127-136.)
［10］WU Y, PAN Z, WU W. Image thresholding based on two-dimensional histogram oblique segmentation and its fast recurring algorithm ［J］. Journal on Communications, 2008,29(4):77-83.(吴一全,潘喆,吴文怡.二维直方图区域斜分阈值分割及快速递推算法［J］.通信学报,2008,29(4):77-83.)
［11］LIU J, LI W. The automatic thresholding of gray-level picture via 2D Otsu method [J]. Acta Automatica Sinica, 1993,19(1):101-105.(刘建庄,栗文清.灰度图像的二维Otsu自动阈值分割法[J].自动化学报,1993,19(1):101-105.)
［12］HAO Y, ZHU F. Fast algorithm for two-dimensional Otsu adaptive threshold algorithm ［J］. Journal of Image and Graphics, 2005,10(4):484-488.(郝颖明,朱枫.2维Otsu自适应阈值的快速算法［J］.中国图象图形学报,2005,10(4):484-488.)
［13］ZHANG H, PENG Q. A survey on digital image inpainting ［J］. Journal of Image and Graphics, 2007,12(1):1-10.(张红英,彭启琮.数字图像修复技术综述［J］.中国图象图形学报,2007,12(1):1-10.)
［14］HOU Z, HE Y, XU W. A fast algorithm of image inpainting ［J］. Journal of Image and Graphics, 2007,12(10):1909-1912.(侯正信,何宇清,许微.一种快速的图像修复算法［J］.中国图象图形学报,2007,12(10):1909-1912.)
［15］LI J, MA Y, ZHAO F, et al.A novel arithmetic of image edge detection of canny operator ［J］. Acta Photonica Sinica, 2011,40(1):50-54.(李俊山,马颖,赵方舟,等.改进的Canny图像边缘检测算法［J］.光子学报,2011,40(1):50-54.)
［16］TANG H, TANG S, SHAO Y. The thinning of linear stripe image ［J］. Journal of Harbin University of Science and Technology, 2002,7(4):50-53.(唐海勇,唐世钢,邵岩.线条纹图像的细化［J］.哈尔滨理工大学学报,2002,7(4):50-53.)

[1]	张佳慧李晓明张嘉祥. 强化形态感知的路面缺陷检测算法[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[2]	况世雄姚俊波陆佳炜王琪冰肖刚. 基于动态图卷积网络的电梯乘客异常行为数据增强方法[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[3]	康斌陈斌王俊杰李昱林赵军智咸伟志. 基于多粒度共享语义中心关联的文本到人物检索方法[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[4]	王子怡李卫军刘雪洋丁建平刘世侠苏易礌. 基于Swin Transformer与多尺度特征融合的图像描述方法#br# [J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[5]	付可意, 王高才, 邬满. 基于改进区域提议网络和特征聚合小样本目标检测方法[J]. 《计算机应用》唯一官方网站, 2024, 44(12): 3790-3797.
[6]	庞玉东, 李志星, 刘伟杰, 李天昊, 王宁宁. 基于改进实时检测Transformer的塔机上俯视场景小目标检测模型[J]. 《计算机应用》唯一官方网站, 2024, 44(12): 3922-3929.
[7]	赵欣, 李鑫杰, 徐健, 刘步云, 毕祥. 基于卷积神经网络与Transformer并行的医学图像配准模型[J]. 《计算机应用》唯一官方网站, 2024, 44(12): 3915-3921.
[8]	颜承志陈颖钟凯高寒. 基于多尺度网络与轴向注意力的3D目标检测算法[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[9]	王静刘嘉星宋婉莹薛嘉兴丁温欣. 基于空间变换和特征分布校准的小样本皮肤图像分类模型[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[10]	廖炎华鄢元霞潘文林. 基于YOLOv9的交通路口图像的多目标检测算法[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[11]	谢斌红剌颖坤张英俊张睿. 自步学习指导下的半监督目标检测框架[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[12]	胡立华, 李小平, 胡建华, 张素兰. 基于四叉树先验辅助的多视图立体方法[J]. 《计算机应用》唯一官方网站, 2024, 44(11): 3556-3564.
[13]	顾聪, 段其强, 任思雨. 基于上下文感知网络的息肉分割算法[J]. 《计算机应用》唯一官方网站, 2024, 44(11): 3617-3622.
[14]	邹耀斌, 张彬. 四向加权香农熵最大化导向的自动阈值分割方法[J]. 《计算机应用》唯一官方网站, 2024, 44(11): 3565-3573.
[15]	刘涛, 鞠事宏, 高一萌. 基于改进YOLOv8n的无人机视角下小目标检测算法[J]. 《计算机应用》唯一官方网站, 2024, 44(11): 3603-3609.

基于图像检测的双绞线绕距测量方法

Pitch measurement methed of twisted-pair wire based on image detection

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics