Visual detection of tobacco packaging film based on apparent features

The main purpose of this article is to study the detection of transparent film on the surface of tobacco packs. Tobacco production line needs an industrial robot to remove the transparent film in the process of unpacking. Therefore, after the industrial robot removes the transparent film, it is nece...

Full description

Bibliographic Details
Main Authors: Zhenxun Jin, Fengyan Zhong, Qiang Zhang, Weisong Wang, Xuanyin Wang
Format: Article
Language:English
Published: SAGE Publishing 2021-06-01
Series:International Journal of Advanced Robotic Systems
Online Access:https://doi.org/10.1177/17298814211024839
id doaj-b42f58e904a6442a8cab08b839794a15
record_format Article
spelling doaj-b42f58e904a6442a8cab08b839794a152021-06-29T23:03:46ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142021-06-011810.1177/17298814211024839Visual detection of tobacco packaging film based on apparent featuresZhenxun Jin0Fengyan Zhong1Qiang Zhang2Weisong Wang3Xuanyin Wang4 China Tobacco Zhejiang Industrial Co., Ltd, Hangzhou, China Zhejiang University, Hangzhou, China China Tobacco Zhejiang Industrial Co., Ltd, Hangzhou, China China Tobacco Zhejiang Industrial Co., Ltd, Hangzhou, China Zhejiang University, Hangzhou, ChinaThe main purpose of this article is to study the detection of transparent film on the surface of tobacco packs. Tobacco production line needs an industrial robot to remove the transparent film in the process of unpacking. Therefore, after the industrial robot removes the transparent film, it is necessary to use machine vision technology to determine whether there is transparent film residue on the surface of tobacco packaging. In this article, based on the study of the optical features of semitransparent objects, an algorithm for detecting the residue of transparent film in tobacco packs based on surface features is proposed. According to the difference of surface features between tobacco and film, a probability distribution model considering highlights, saturation, and texture density is designed. Because the probability distribution model integrates many features of tobacco and film, it is more reasonable to distinguish the tobacco film regions. In this article, an appropriate foreground box with a trapezoidal mask and image segmentation algorithm GrabCut is used to segment the foreground area of tobacco pack more accurately, and the possible film area is obtained by image differential and morphological processing. Finally, on the basis of comparing the effect of various machine learning algorithms on the image classification of possible film regions, support vector machine based on color features is used to judge the possible film region. Application results of the system show that the method proposed in this article can effectively detect whether there is film residue on the surface of tobacco pack.https://doi.org/10.1177/17298814211024839
collection DOAJ
language English
format Article
sources DOAJ
author Zhenxun Jin
Fengyan Zhong
Qiang Zhang
Weisong Wang
Xuanyin Wang
spellingShingle Zhenxun Jin
Fengyan Zhong
Qiang Zhang
Weisong Wang
Xuanyin Wang
Visual detection of tobacco packaging film based on apparent features
International Journal of Advanced Robotic Systems
author_facet Zhenxun Jin
Fengyan Zhong
Qiang Zhang
Weisong Wang
Xuanyin Wang
author_sort Zhenxun Jin
title Visual detection of tobacco packaging film based on apparent features
title_short Visual detection of tobacco packaging film based on apparent features
title_full Visual detection of tobacco packaging film based on apparent features
title_fullStr Visual detection of tobacco packaging film based on apparent features
title_full_unstemmed Visual detection of tobacco packaging film based on apparent features
title_sort visual detection of tobacco packaging film based on apparent features
publisher SAGE Publishing
series International Journal of Advanced Robotic Systems
issn 1729-8814
publishDate 2021-06-01
description The main purpose of this article is to study the detection of transparent film on the surface of tobacco packs. Tobacco production line needs an industrial robot to remove the transparent film in the process of unpacking. Therefore, after the industrial robot removes the transparent film, it is necessary to use machine vision technology to determine whether there is transparent film residue on the surface of tobacco packaging. In this article, based on the study of the optical features of semitransparent objects, an algorithm for detecting the residue of transparent film in tobacco packs based on surface features is proposed. According to the difference of surface features between tobacco and film, a probability distribution model considering highlights, saturation, and texture density is designed. Because the probability distribution model integrates many features of tobacco and film, it is more reasonable to distinguish the tobacco film regions. In this article, an appropriate foreground box with a trapezoidal mask and image segmentation algorithm GrabCut is used to segment the foreground area of tobacco pack more accurately, and the possible film area is obtained by image differential and morphological processing. Finally, on the basis of comparing the effect of various machine learning algorithms on the image classification of possible film regions, support vector machine based on color features is used to judge the possible film region. Application results of the system show that the method proposed in this article can effectively detect whether there is film residue on the surface of tobacco pack.
url https://doi.org/10.1177/17298814211024839
work_keys_str_mv AT zhenxunjin visualdetectionoftobaccopackagingfilmbasedonapparentfeatures
AT fengyanzhong visualdetectionoftobaccopackagingfilmbasedonapparentfeatures
AT qiangzhang visualdetectionoftobaccopackagingfilmbasedonapparentfeatures
AT weisongwang visualdetectionoftobaccopackagingfilmbasedonapparentfeatures
AT xuanyinwang visualdetectionoftobaccopackagingfilmbasedonapparentfeatures
_version_ 1721354250886316032