Automated Visual Inspection of Glass Bottle Bottom With Saliency Detection and Template Matching
Automated Visual Inspection of Glass Bottle Bottom With Saliency Detection and Template Matching
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DOI：
10.1109/tim.2018.2886977
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发表时间：
2019-01
影响因子：
5.6
通讯作者：
Xianen Zhou;Yaonan Wang;Changyan Xiao;Qing Zhu;Xiao Lu;Hui Zhang;Ji Ge;Huihuang Zhao
中科院分区：
文献类型：
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作者：
Xianen Zhou;Yaonan Wang;Changyan Xiao;Qing Zhu;Xiao Lu;Hui Zhang;Ji Ge;Huihuang Zhao
玻璃瓶广泛用作食品和饮料行业的容器，特别是用于啤酒和碳酸饮料。作为玻璃瓶的关键部件，瓶底及其质量与产品安全密切相关。因此，在瓶子用于包装之前必须检查瓶底。本文设计了一种基于机器视觉的实时瓶底检测装置，提出了一种主要利用显著性检测和模板匹配的缺陷检测框架。在简要描述了该装置之后，我们的重点是图像分析。首先，将Hough圆检测与尺寸先验相结合进行底部定位，并将感兴趣区域划分为三个测量区域：中心面板区域、环形面板区域和环形纹理区域。然后，提出了一种显著性检测方法，用于发现中心面板区域内的缺陷区域。采用多尺度滤波方法对环形面元区域进行缺陷搜索。对于环形纹理区域，采用联合收割机模板匹配与多尺度滤波相结合的方法进行缺陷检测。最后，融合三个测量区域的缺陷检测结果，以区分被测瓶底的质量。建议的缺陷检测框架进行评估瓶底图像由我们设计的设备。实验结果表明，所提出的方法实现了最好的性能相比，许多传统的方法。
Glass bottles are widely used as containers in the food and beverage industry, especially for beer and carbonated beverages. As the key part of a glass bottle, the bottle bottom and its quality are closely related to product safety. Therefore, the bottle bottom must be inspected before the bottle is used for packaging. In this paper, an apparatus based on machine vision is designed for real-time bottle bottom inspection, and a framework for the defect detection mainly using saliency detection and template matching is presented. Following a brief description of the apparatus, our emphasis is on the image analysis. First, we locate the bottom by combining Hough circle detection with the size prior, and we divide the region of interest into three measurement regions: central panel region, annular panel region, and annular texture region. Then, a saliency detection method is proposed for finding defective areas inside the central panel region. A multiscale filtering method is adopted to search for defects in the annular panel region. For the annular texture region, we combine template matching with multiscale filtering to detect defects. Finally, the defect detection results of the three measurement regions are fused to distinguish the quality of the tested bottle bottom. The proposed defect detection framework is evaluated on bottle bottom images acquired by our designed apparatus. The experimental results demonstrate that the proposed methods achieve the best performance in comparison with many conventional methods.