文章摘要
杨健,辛浪,豆昌军.基于机器视觉技术的瓶盖划痕检测[J].包装工程,2019,40(13):227-232.
YANG Jian,XIN Lang,DOU Chang-jun.Bottle Cap Scratch Detection Based on Machine Vision Technology[J].Packaging Engineering,2019,40(13):227-232.
基于机器视觉技术的瓶盖划痕检测
Bottle Cap Scratch Detection Based on Machine Vision Technology
投稿时间:2019-04-01  修订日期:2019-07-10
DOI:10.19554/j.cnki.1001-3563.2019.13.033
中文关键词: 划痕检测  机器视觉  梯度双边滤波  模板匹配  高斯差分滤波  二维Otsu阈值分割
英文关键词: scratch detection  machine vision  gradient bilateral filtering  template matching  Gaussian difference filtering  two-dimensional Otsu threshold segmentation
基金项目:
作者单位
杨健 成都理工大学成都 610059 
辛浪 成都理工大学成都 610059 
豆昌军 成都理工大学成都 610059 
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中文摘要:
      目的 针对当前国内瓶盖划痕检测算法在较复杂背景条件下,存在精度不高、瓶盖划痕图像灰度值变化剧烈,且影响因素较多,无法准确定位检测的问题,提出一种基于机器视觉技术的瓶盖划痕检测方案,以实现在对比度较低的背景下对细微划痕快速准确的检测。方法 对标准图像作预处理创建模板,对样本图像进行滤波降噪、基于形状的模板匹配、提取感兴趣区域(ROI)、高斯差分滤波增强划痕拉开对比度、二维Otsu阈值分割、形态学处理、特征提取划痕。通过获取300幅瓶盖表面图像,与差影法、大津法、人工检测法进行了划痕检测对比实验。结果 实验结果表明,提出的算法能快速、准确、高效地提取瓶盖划痕,检测1幅图片的平均时间为113 ms,检测准确率为98.3%。结论 该方案与人工检测、差影法、大津法相比,检测精度更高、速度更快、鲁棒性更好,可以满足工业上的生产需求。
英文摘要:
      The work aims to propose a bottle cap scratch detection scheme based on machine vision technology so as to achieve fast and accurate detection of fine scratches in a low contrast background since the current domestic bottle cap scratch detection methods do not have high precision under the complicated background conditions, the gray value of the bottle scratch image is changed sharply, and many influencing factors cannot be accurately positioned. The standard image was preprocessed to create the template, and the sample image was filtered to reduce noise. The template was matched according to shape, the region of interest (ROI) was extracted, Gaussian difference filtering was used to enhance the contrast of the scratch, the two-dimensional Otsu threshold segmentation was performed, morphological processing was conducted, and scratches were extracted according to features. By obtaining the image of the surface of 300 caps, the methods of aberration, Dajin and manual detection were used to compare the scratches. The experimental results showed that the proposed algorithm could extract the cap scratches quickly, accurately and efficiently. The average time for detecting a picture was 113 ms, and the detection accuracy was 98.3%. Compared with manual detection, differential image method and Dajin method, the scheme has higher detection precision, faster speed and better robustness, and meets industrial production requirements.
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