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首页> 外文期刊>International Journal of Computer Integrated Manufacturing >Visual inspection of glass bottlenecks by multiple-view analysis
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Visual inspection of glass bottlenecks by multiple-view analysis

机译：通过多视图分析目视检查玻璃瓶颈

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摘要

The narrow structure of bottlenecks poses a very challenging problem for automated visual inspection systems and surprisingly, this issue has received little attention in literature. Bottleneck inspection is highly relevant to the fabrication of glass bottles, e.g., for the wine and beer industry. Defects in glass bottles can arise in various situations such as an incomplete reaction in a batch, batch contaminants and interactions of the melted material among others. This paper presents an inspection approach that utilises geometry of multiple views along with a rich set of feature descriptors to discriminate real flaws from false alarms in uncalibrated images of glass bottlenecks. The proposed method is based on an automatic multiple view inspection (AMVI) technique for the automatic detection of flaws. This technique involves an initial step that extracts numerous segmented regions from a set of views of the object under inspection. These regions are subsequently classified either as real flaws or as false alarms. The classification process considers that image noise and false alarms occur as random events in different views while real flaws induce geometric and featural relations in the views where they appear. Therefore, by analysing such relations it is possible to successfully localise real flaws and to discard a large number of false alarms. An important characteristic of the proposed methodology is the complete lack of camera calibration which makes our method very suitable for applications where camera calibration is difficult or expensive to carry out. Our inspection system achieves a true positive rate of 99.1% and a false positive rate of 0.9%.
机译：瓶颈的狭窄结构给自动外观检查系统带来了非常具有挑战性的问题，令人惊讶的是，这个问题在文献中很少受到关注。瓶颈检查与玻璃瓶的制造高度相关，例如在葡萄酒和啤酒行业。玻璃瓶中的缺陷会在各种情况下出现，例如批处理中的反应不完全，批处理中的污染物以及熔融材料之间的相互作用等。本文提出了一种检查方法，该方法利用多个视图的几何形状以及一组丰富的特征描述符来区分玻璃瓶颈未校准图像中的真实缺陷和错误警报。所提出的方法基于用于自动检测缺陷的自动多视图检查（AMVI）技术。该技术涉及一个初始步骤，该步骤从被检查对象的一组视图中提取许多分割的区域。这些区域随后被分类为真实缺陷或错误警报。分类过程认为图像噪声和误报是在不同视图中作为随机事件发生的，而实际缺陷会在它们出现的视图中引起几何和特征关系。因此，通过分析这种关系，可以成功地定位实际缺陷并丢弃大量错误警报。所提出的方法的一个重要特征是完全缺乏相机校准，这使得我们的方法非常适合于相机校准困难或昂贵的应用。我们的检查系统实现了99.1％的真实阳性率和0.9％的错误阳性率。

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