Abstract. In recent years, driven by new standards and image brand, product quality has become highly critical in the food industry. Particularly concerning the beverage industry, the potential presence of very small foreign objects, especially glass fragments, has to be checked. The visible optical domain offers an advantageous alternative solution to the expensive and intrusive x-ray systems. However, such a solution requires the development of robust processing algorithms satisfying real-time industrial constraints, which is a very challenging task. A detection method based on kurtosis and local information that emphasizes connected components is proposed, as well as two robust criteria that give a greater robustness to the detector. A specific kurtosis is calculated using an inclined frame definition that is based on both time and spatial dimensions. Such kurtosis increases the detection capacity of the moving objects and allows the estimation of their direction without adding more costs. The kurtosis being very sensitive to outliers, noisy objects that are very small are filtered, allowing efficient detection of foreign objects, such as glass fragments, as long as they are bigger than the noisy objects. In case of big foreign objects, the low sensitivity of the kurtosis is compensated for by the large detected surface. The proposed detection method can be directly applied on video sequences acquired by a standard RGB camera in industrial environments. The experimental results show the effectiveness of the method in detecting real random foreign objects, regardless of their size or their transparency, in various semiopaque bottles.

中文翻译：

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基于峰度的连通分量检测半透明瓶中小透明异物的方法

摘要。近年来，在新标准和形象品牌的推动下，产品质量在食品行业变得至关重要。特别是对于饮料行业，必须检查可能存在非常小的异物，尤其是玻璃碎片。可见光域为昂贵且侵入性的 X 射线系统提供了一种有利的替代解决方案。然而，这样的解决方案需要开发满足实时工业约束的鲁棒处理算法，这是一项非常具有挑战性的任务。提出了一种基于峰态和局部信息的检测方法，强调连通分量，以及两个鲁棒性标准，为检测器提供了更大的鲁棒性。使用基于时间和空间维度的倾斜框架定义计算特定峰度。这种峰度增加了移动物体的检测能力，并允许在不增加更多成本的情况下估计它们的方向。峰度对异常值非常敏感，非常小的嘈杂物体会被过滤掉，只要它们比嘈杂的物体大，就可以有效地检测异物，例如玻璃碎片。在异物较大的情况下，峰度的低灵敏度通过较大的检测表面得到补偿。所提出的检测方法可以直接应用于工业环境中标准 RGB 相机获取的视频序列。实验结果表明了该方法在检测真实随机异物方面的有效性，