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Vision-Based Automatic Detection Method for Suspended Matter in Bottled Mineral Water

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瓶装矿泉水在出厂前需要检测里面是否存在悬浮物颗粒,目前主要是靠人工观察,这种方法费时费力,而且依赖人工的主观感觉,检测效果不好。针对这一问题,基于机器视觉技术,建立了一种瓶装矿泉水中悬浮物颗粒自动检测方法,包括图像采集、悬浮物颗粒目标识别、数量统计及尺寸参数检测等图像分析处理流程。在此基础上,设计了瓶装矿泉水中悬浮物颗粒自动检测装置,阐述了该装置的结构及工作原理,进行了瓶装矿泉水中悬浮物颗粒检测实验。检测实验结果表明,本文提出的方法可从定性和定量两个方面对瓶装矿泉水中是否存在悬浮物颗粒进行检测,悬浮物颗粒数量统计准确,悬浮物颗粒的尺寸检测的最大误差为0.28 mm,相对误差小于6.8%,具有较高的精确度。将上述装置及方法用于瓶装矿泉水出厂前检测,具有检测准确、节约人力、工作效率高、操作简单的特点。


Bottled mineral water must be tested to determine the presence of suspended particles prior to being dispatched from the factory. At present, manual methods are used toward this end. These methods are time-consuming and laborious, relying on artificial subjective feelings, and their detection results are not satisfactory. Aiming at this problem, an automatic detection method for suspended particles in bottled mineral water based on computer-vision technology is presented in this paper, which includes image acquisition, recognition of suspended particles, quantity statistics, size parameter detection, and other image analysis processing. On this basis, we develop an automatic detection device for suspended matter in bottled mineral water, describing the structure and working principle of the device, and accomplish the inspection test of suspended matter in bottled mineral water. Results illustrate that the proposed method can both qualitatively and quantitatively detect the number and size of suspended particles in bottled mineral water. The quantitative statistics of suspended particles is accurate. The maximum testing error for the size of suspended particles is 0.28 mm, and the relative errors are less than 6.8%. The proposed device and method could be applied to the detection of bottled mineral water prior to its dispatch from the factory with the characteristic features of accurate detection, saving labor, improving work efficiency, and easy operation.

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作者单位    点击查看

盛子夜:昆明理工大学信息工程与自动化学院, 云南 昆明 650500
张云伟:昆明理工大学信息工程与自动化学院, 云南 昆明 650500

联系人作者:盛子夜(1538051646@qq.com); 张云伟(1657824262@qq.com);


【1】Cao Y R, Qin Z J, Liang H P. The importance of water quality testing for drinking water China Food Safety Magazine[J]. 0, 2017(24): 74-75.
曹毅荣, 覃志杰, 梁辉鹏. 生活饮用水水质检测的重要性 食品安全导刊[J]. 0, 2017(24): 74-75.

【2】Chen H, Liu Y M, Zou J Y, et al. Research status and development trends of fiber optical technology for water quality monitoring [J]. Laser & Optoelectronics Progress. 2015, 52(3): 030006.
陈浩, 刘月明, 邹建宇, 等. 光纤水质检测技术的研究现状与发展趋势 [J]. 激光与光电子学进展. 2015, 52(3): 030006.

【3】Tang Y P, Yan H H, Huang L L, et al. -04-03 [P]. high-speed automatic particle counting device based on machine vision: CN103020707A. 2013.
汤一平, 严杭晨, 黄磊磊, 等. -04-03 [P]. . 基于机器视觉的流水式高精度、高速的颗粒自动计数装置: CN103020707A . 2013.

【4】Wang D Q, Yu W, Lei L B, its device: CN103226088A[P], et al. -07-31 . 2013.
汪地强, 余苓, 雷良波, 等. -07-31 [P]. . 一种颗粒计数方法及其装置: CN103226088A. 2013.

【5】Lam P J, Lee J M, Heller M I, et al. Size-fractionated distributions of suspended particle concentration and major phase composition from the US GEOTRACES Eastern Pacific Zonal Transect (GP16) [J]. Marine Chemistry. 2018, 201: 90-107.

【6】Carter R M, Yan Y. Measurement of particle shape using digital imaging techniques [J]. Journal of Physics: Conference Series. 2005, 15: 177-182.

【7】Zhao J. Particle shape analysis of black carbon particles (nano size level) using digital image processing [D]. Shanghai: East China University of Science and Technology. 2014.
赵金. 大气中纳米级黑碳颗粒物形貌的数字图像处理研究 [D]. 上海: 华东理工大学. 2014.

【8】Li L. Research on the recognition algorithm for concentration and diameter distribution of indoor suspended particulate [D]. Wuhan: Wuhan University of Technology. 2008.
李莉. 室内悬浮颗粒物浓度与粒度图像识别算法的研究 [D]. 武汉: 武汉理工大学. 2008.

【9】Hu Y S, Wu Y, Luo Q J, et al. -11-20 . 2013.
胡阳生, 吴悠, 罗奇军, 等. -11-20 [P]. . 基于显微图像处理的大气颗粒检测装置:CN203299089U. 2013.

【10】Wang H Y, Zhang Y H. Particle identification count based on machine vision [J]. Journal of Changchun Institute of Technology(Natural Sciences Edition). 2013, 14(4): 101-104.
王海燕, 张瑜慧. 基于机器视觉的颗粒识别计数 [J]. 长春工程学院学报(自然科学版). 2013, 14(4): 101-104.

【11】Wang J. Study on granularity measurement method using image processing Xi''''an: Xi''''an University of [D]. Technology. 2006.
王建. 基于图像处理的颗粒度测量方法研究 [D]. 西安: 西安理工大学. 2006.

【12】Wang W Y, Yang K C, Luo M, et al. Measurement of three-dimensional volume scattering function of suspended particles in water [J]. Acta Optica Sinica. 2018, 38(3): 0329001.
王万研, 杨克成, 罗曼, 等. 水中悬浮颗粒的三维体散射函数测量 [J]. 光学学报. 2018, 38(3): 0329001.

【13】Cai C D, Huo G Y, Zhou Y, et al. Underwater image restoration method based on scene depth estimation and white balance [J]. Laser & Optoelectronics Progress. 2019, 56(3): 031008.
蔡晨东, 霍冠英, 周妍, 等. 基于场景深度估计和白平衡的水下图像复原 [J]. 激光与光电子学进展. 2019, 56(3): 031008.

【14】O?mann B E, Sarau G, Holtmannsp?tter H, et al. Small-sized microplastics and pigmented particles in bottled mineral water [J]. Water Research. 2018, 141: 307-316.

【15】Kang M. Research on several key algorithms in image processing Xi''''an: [D]. Xidian University. 2009.
康牧. 图像处理中几个关键算法的研究 [D]. 西安: 西安电子科技大学. 2009.

【16】Nnolim U A. An adaptive RGB colour enhancement formulation for logarithmic image processing-based algorithms [J]. Optik. 2018, 154: 192-215.

【17】Terol-Villalobos I R, Mendiola-Santibá?ez J D, Canchola-Magdaleno S L. Image segmentation and filtering based on transformations with reconstruction criteria [J]. Journal of Visual Communication and Image Representation. 2006, 17(1): 107-130.

【18】Wessely B, Gabsch S, Altmann J, et al. Single particle detection and size analysis with statistical methods from particle imaging data [J]. Particle & Particle Systems Characterization. 2006, 23(2): 165-169.


Sheng Ziye,Zhang Yunwei. Vision-Based Automatic Detection Method for Suspended Matter in Bottled Mineral Water[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141028

盛子夜,张云伟. 基于机器视觉的瓶装矿泉水悬浮物自动检测方法[J]. 激光与光电子学进展, 2020, 57(14): 141028

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