文章摘要
张彩霞,陈晓荣,徐云洁,卫志豪,周书辰.玻璃表面缺陷检测系统研究[J].包装工程,2020,41(13):216-222.
ZHANG Cai-xia,CHEN Xiao-rong,XU Yun-jie,WEI Zhi-hao,ZHOU Shu-chen.Surface Defect Detection System of Glass[J].Packaging Engineering,2020,41(13):216-222.
玻璃表面缺陷检测系统研究
Surface Defect Detection System of Glass
投稿时间:2019-12-05  修订日期:2020-07-10
DOI:10.19554/j.cnki.1001-3563.2020.13.031
中文关键词: 背光照射  缺陷检测  边缘检测  改进K-means聚类  Otsu阈值分割  补偿系数
英文关键词: backlight illumination  defect detection  edge detection  improved K-means clustering  Otsu threshold segmentation  compensation coefficient
基金项目:
作者单位
张彩霞 上海理工大学上海 200093 
陈晓荣 上海理工大学上海 200093 
徐云洁 上海理工大学上海 200093 
卫志豪 上海理工大学上海 200093 
周书辰 上海理工大学上海 200093 
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中文摘要:
      目的 针对玻璃表面存在的划痕、漏点、结石和水印等4种主要缺陷,研究一种基于背光照射的玻璃表面缺陷检测方法,提出一种改进K均值聚类算法用于水印缺陷的检测。方法 首先,通过设计的图像采集系统对玻璃图像进行采集,并对采集图像背景估计;然后根据水印缺陷与其他3种缺陷的灰度差异,将含缺陷的玻璃分成2类,完成缺陷粗分类;接着利用边缘检测算法对含划痕、漏点和结石缺陷的玻璃图像进行处理,利用结合了Otsu阈值分割方法和补偿系数f的改进K均值聚类算法对含有水印缺陷的玻璃图像进行处理,最终实现对玻璃表面4种缺陷的识别与标记。结果 实验表明,该系统操作方便,算法复杂度低,缺陷识别准确度高,检测速度快。结论 通过上述玻璃表面缺陷检测系统,可准确高效地检测出玻璃表面存在的4种主要缺陷。改进的K均值聚类可以准确实现对水印缺陷的检测,且该方法可以克服聚类迭代次数高,聚类结果容易陷入局部最小等缺点。极大地提高了缺陷检测的效率,可用于玻璃生产过程中的实时检测。
英文摘要:
      Aiming at the four major defects such as scratches, missing points, stones and watermarks on the glass surface, the work aims to study a method for detecting surface defects of glass based on backlight illumination, and propose an improved K-means clustering algorithm for watermark defect detection. First, the glass image was acquired by the designed image acquisition system, and the background of the acquired image was estimated. Then, based on the gray difference between the watermark defect and the other three types of defects, the defective glass was divided into two categories to complete the rough classification of the defects. Next, the edge detection algorithm was used to process the glass image with such defects as scratches, missing points and stones, and the improved K-means clustering algorithm combining Otsu threshold segmentation method and compensation coefficient f was used to process the glass image with watermarks. Finally, the identification and marking of four defects on the glass surface were completed. The experiments showed that the system was easy to operate, the algorithm was low in complexity, the accuracy of defect identification was high, and the detection speed was fast. Through the above-mentioned glass surface defect detection system, four major defects on the glass surface can be accurately and efficiently detected. The improved K-means clustering can accurately detect watermarks. It overcomes the shortcomings such as a huge number of cluster iterations and clustering results easy to fall into the local minimum. As the proposed method greatly improves the efficiency of defect detection, it can be used for the real-time detection in the process of glass production.
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