Design of counter for cigarette packet based on machine vision

doi:10.3778/j.issn.1002-8331.2010.26.019

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (26): 58-59.DOI: 10.3778/j.issn.1002-8331.2010.26.019

• 研发、设计、测试 • Previous Articles Next Articles

Design of counter for cigarette packet based on machine vision

SHI Hui¹，YU Wen-yong²

1.School of Mechatronic Engineering，Wuhan University of Technology，Wuhan 430070，China
2.School of Mechanical Science and Engineering，Huazhong University of Science and Technology，Wuhan 430074，China

Received:2009-09-21 Revised:2009-11-20 Online:2010-09-11 Published:2010-09-11
Contact: SHI Hui1，YU Wen-yong2

新型烟标计数装置的设计

石绘¹，余文勇²

1.武汉理工大学机电学院包装工程系，武汉 430070
2.华中科技大学数字制造装备与技术国家重点实验室，武汉 430074

通讯作者: 石绘

Abstract

Abstract: It’s necessary to count the cigarette packet before sending to cigarette manufacturing enterprise.In this paper，a counter is designed to realize this work.Firstly，the image of the side of a pile of cigarette packet should be obtained by a scanner.The pretreatment process should be operated to smooth the image，including wiping off background and Gauss filter.Sobel operators is adopted to edge detection and the counting can be realized by progressive scan.It is proved that it needs only five seconds to count once using this system，which is more efficient and more precision.

Key words: machine vision, cigarette packet, counting, Gauss filter, Sobel operators

摘要： 烟标在销往香烟生产企业之前，需要对其进行计数。设计了一种基于机器视觉的烟标计数装置。采用特制扫描仪，将成叠的烟标侧面转换成图像信号，传送给专用的图像处理系统；图像系统通过去除图像背景和高斯滤波等操作，进行图像的预处理；采用Sobel算子进行图像边缘检测；逐行扫描，以此来计算烟标的数目。实验证明利用该装置，能实现精确计数，同时操作简单，计数迅速，实验中操作一次仅需5秒钟。

关键词: 机器视觉, 烟标, 计数, 高斯滤波, Sobel算子

CLC Number:

TP242.6+2

SHI Hui¹，YU Wen-yong². Design of counter for cigarette packet based on machine vision[J]. Computer Engineering and Applications, 2010, 46(26): 58-59.

石绘¹，余文勇². 新型烟标计数装置的设计[J]. 计算机工程与应用, 2010, 46(26): 58-59.

[1]	PENG Zhaoyong, WU Quan, CHEN Huawei, ZHENG Yue, WANG Shuxiang. Review of Research on Machine Vision Defect Detection Based on Literature Measurement [J]. Computer Engineering and Applications, 2021, 57(4): 28-34.
[2]	CHANG Yawen, ZHAO Dongqing, SHAN Yanhu. Research on Stereo Matching Algorithm Based on Multi-feature Fusion and Adaptive Aggregation [J]. Computer Engineering and Applications, 2021, 57(23): 219-225.
[3]	ZUO Jianhao, JIANG Wengang. Adaptive Feature Fusion Network for Crowd Counting [J]. Computer Engineering and Applications, 2021, 57(21): 203-208.
[4]	XU Yangyang，LI Wei，WANG Jie. Optimization of Robot Delay Using Events and Deadline Control [J]. Computer Engineering and Applications, 2021, 57(17): 260-268.
[5]	LI Zhonghai, BAI Qiuyang, WANG Fuming, LIU Hairong. Real-Time Detection System of Rail Surface Defects Based on Semantic Segmentation [J]. Computer Engineering and Applications, 2021, 57(12): 248-256.
[6]	GAO Weijun, SHI Yang, YANG Jie, ZHANG Chunxia. An Improved Lightweight Head Detection Method [J]. Computer Engineering and Applications, 2021, 57(1): 207-212.
[7]	LIU Yu, CHEN Zhe, LI Mengheng, QIN Tuanfa. Scheme for Information Security in Body Area Networks Based on Alliance Blockchain [J]. Computer Engineering and Applications, 2020, 56(4): 57-62.
[8]	WU Qingke, WU Xiao, YUAN Yuyang, GUAN Xinqiang. Crowd Counting Combined with Neural Networks and Multi-Column Feature Map Aggregation [J]. Computer Engineering and Applications, 2020, 56(4): 214-218.
[9]	ZHAO Yan, ZUO Baoqi. Analysis on Application of Machine Vision in Fabric Defect Detection [J]. Computer Engineering and Applications, 2020, 56(2): 11-17.
[10]	HE Wenbin, WEI Aiyun, MING Wuyi, JIA Haojie. Survey of Fruit Quality Detection Based on Machine Vision [J]. Computer Engineering and Applications, 2020, 56(11): 10-16.
[11]	MA Dongmei, HE Sansan, YANG Caifeng, YAN Chunman. Semantic Segmentation Based on Convolutional Neural Networks with Feature Fusion [J]. Computer Engineering and Applications, 2020, 56(10): 193-198.
[12]	NAN Hao, TONG Minglei, FAN Lvyuan, LI Min. Multi-Task Multi-Level Convolutional Neural Network for Application of Crowd Counting [J]. Computer Engineering and Applications, 2019, 55(23): 182-187.
[13]	WANG Yuyuan, XU Jie, JI Weixi. Intelligent Recognition Method for Geometric Features of Parts Based on Supervised Machine Learning [J]. Computer Engineering and Applications, 2019, 55(22): 225-230.
[14]	SUN Weihong1, LI Qiankun1, SHAO Tiefeng1, WU Huiming2. Crack Detection Algorithm of Protective Wall for Piles Based on Machine Vision [J]. Computer Engineering and Applications, 2019, 55(14): 260-265.
[15]	LI Chao, SUN Jun. Effective method of weld defect detection and classification based on machine vision [J]. Computer Engineering and Applications, 2018, 54(6): 264-270.

Design of counter for cigarette packet based on machine vision

新型烟标计数装置的设计

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics