[1]董世超,陈丙三,连长伟,等.基于机器视觉的烟梗长度测量方法[J].福建理工大学学报,2024,22(01):74-81.[doi:10.3969/j.issn.2097-3853.2024.01.011]
 DONG Shichao,CHEN Bingsan,LIAN Changwei,et al.Measurement method of tobacco stem length based on machine vision[J].Journal of Fujian University of Technology;,2024,22(01):74-81.[doi:10.3969/j.issn.2097-3853.2024.01.011]
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基于机器视觉的烟梗长度测量方法
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《福建理工大学学报》[ISSN:2097-3853/CN:35-1351/Z]

卷:
第22卷
期数:
2024年01期
页码:
74-81
栏目:
出版日期:
2024-02-25

文章信息/Info

Title:
Measurement method of tobacco stem length based on machine vision
作者:
董世超陈丙三连长伟卢敏瑞彭小冬张腾健王芳
福建理工大学机械与汽车工程学院
Author(s):
DONG Shichao CHEN Bingsan LIAN Changwei LU Minrui PENG Xiaodong ZHANG Tengjian WANG Fang
School of Mechanical and Automotive Engineering, Fujian University of Technology
关键词:
机器视觉烟梗测量最小外接矩形改进Steger算法
Keywords:
machine vision tobacco stem measurement minimum bounding rectangle improved Steger algorithm
分类号:
TP29
DOI:
10.3969/j.issn.2097-3853.2024.01.011
文献标志码:
A
摘要:
针对传统人工烟梗长度测量方法存在效率低、劳动强度大、精度难以保证等问题,提出了一种利用机器视觉测量不同形态烟梗长度的分类测量法。该方法通过2 500 万像素的高端面阵黑白相机采集烟梗清晰数字图像。首先,通过中值滤波结合图像开运算、全局阈值分割、图像连通性操作等步骤对图像进行预处理并提取烟梗感兴趣区域。然后,根据矩形度将其分为矩形和异形两种类型,以提高测量的准确性。最后,采用基于凸包获取最小外接矩形测量法和基于骨架提取自适应宽度的改进Steger 算法,分别对两种形态的烟梗进行测量。选取15 组长度不同的矩形梗和异形梗,通过三坐标测量仪测量烟梗的标准值与本方法测量值对比。结果表明:相较于骨架提取法和传统Steger 算法,采用该方法对矩形与异形梗测量的最大相对误差分别为-0.19%和-0.124%,对单根烟梗的重复测量最大误差分别为0.008 mm 和0.006 mm,不但具有较高的精度和较好的稳定性,还可显著降低劳动强度,具备广泛的实际应用潜力。
Abstract:
In response to the challenges posed by traditional manual measurements of tobacco stem length, characterized by low efficiency, high labor intensity, and difficulty in ensuring accuracy, a classification measurement method for measuring the length of different forms of tobacco stems by machine vision was proposed. This method collects clear digital images of tobacco stems through a 25 million pixel high-end array black-and-white camera. Initially, the images were subjected to preprocessing through median filtering and image opening operations. Subsequently, global threshold segmentation and image connectivity techniques were applied to extract and crop the region of interest within the tobacco stem. 〖JP3〗Based on morphology, the tobacco stems were then categorized into rectangular and curved types to enhance measurement accuracy. To perform measurements, two distinct approaches were employed for these two morphological categories: the minimum bounding rectangle measurement method for rectangular stems and an improved Steger algorithm based on the adaptive width skeleton extraction for curved stems. Fifteen groups of rectangular stems and curved ones with different lengths were selected, and the standard values of tobacco stems measured by three-coordinate measuring instrument were compared with the measured values of this method. Experimental results demonstrate that the maximum relative errors of rectangular and irregular stems measured by this method are -0.196% and -0.109%, respectively, and the maximum repeated measurement errors for a single tobacco stem are 0.008 mm and 0.006 mm, respectively. This method not only has high accuracy and good stability, but also significantly reduces labor intensity and has broad practical application potential.

参考文献/References:

[1] 李晓,周利军,纪晓楠,等.不同尺寸规格的烟梗吸湿特性及梗丝质量的影响[J].西南农业学报,2017,30(3):675-680.[2] 杨洋,杨雨波,吴昊,等.烟草加工中打叶复烤工艺参数优化[J].农业工程,2018,8(8):83-85.[3] 崔云月,管一弘,孙娜,等.BP神经网络在烟梗长短梗率检测中的应用[J].软件导刊,2021,20(2):63-67.[4] 朱文魁,郭高飞,丁美宙,等.一种基于X射线透射图像定量检测烟梗中粗梗率和长短梗率的测定方法:CN108007945A[P].2018-05-08.[5] 武凯,熊文,卢婷,等.一种烟梗长度或直径的检测方法:CN115031667A[P].2022-09-09.[6] 雷光钰.基于红外热波的缺陷自动检测识别与评估方法研究[D].成都:电子科技大学,2022.[7] 代长安,区昊辰,朱鼎天,等.不规则多边形区域搜索航路规划算法[J].工业技术创新,2021,8(6):102-106,112.[8] 曾凯,刘贺飞,何茜,等.基于改进Steger算法的线结构光中心提取[J].华北理工大学学报(自然科学版),2021,43(1):101-107.[9] 王志永,于宇,王武越,等.基于改进Steger算法流程的线激光中心提取[J].电子测量技术,2023,46(1):84-89.[10] GIULIETTI N, CHIARIOTTI P, REVEL G M. Automated measurement of geometric features in curvilinear structures exploiting Stegers algorithm[J]. Sensors,2023,23(8):4023.[11] 南方,李大华,高强,等.改进Steger算法的自适应光条纹中心提取[J].激光杂志,2018,39(1):85-88.[12] 刁智华,吴贝贝,毋媛媛,等.基于图像处理的骨架提取算法的应用研究[J].计算机科学,2016,43(S1):232-235.[13] 张国栋,韩佳池.基于模糊距离变换的骨架剪枝算法[J].沈阳航空航天大学学报,2012,29(1):64-69.

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更新日期/Last Update: 2024-02-25