An automated and reliable processing of bubbly flow images is highly needed to analyse large data sets of comprehensive experimental series. A particular difficulty arises due to overlapping bubble projections in recorded images, which highly complicates the identification of individual bubbles. Recent approaches focus on the use of deep learning algorithms for this task and have already proven the high potential of such techniques. The main difficulties are the capability to handle different image conditions, higher gas volume fractions and a proper reconstruction of the hidden segment of a partly occluded bubble. In the present work, we try to tackle these points by testing three different methods based on Convolutional Neural Networks (CNNs) for the two former and two individual approaches that can be used subsequently to address the latter. To validate our methodology, we created test data sets with synthetic images that further demonstrate the capabilities as well as limitations of our combined approach. The generated data, code and trained models are made accessible to facilitate the use as well as further developments in the research field of bubble recognition in experimental images.
翻译:为了分析综合实验系列的大型数据集,非常需要自动和可靠地处理泡泡流图像,分析综合实验系列的大型数据集非常需要,由于所录图像中的泡沫预测重叠,造成一个特别困难,使个别泡泡的识别工作高度复杂化。最近的方法侧重于为这项任务使用深学习算法,已经证明了这种技术的高度潜力。主要困难在于处理不同图像状况的能力、高气容量碎片和适当重建部分隐蔽的泡沫的隐藏部分。在目前的工作中,我们试图通过测试三种不同的方法来解决这些点。三种不同的方法基于前两种神经神经网络(CNNs),而后两种方法则可用于处理后者。为了验证我们的方法,我们用合成图像制作了测试数据集,进一步展示了我们综合方法的能力和局限性。所产生的数据、代码和经过培训的模型可以进入,以便利实验图像中泡沫识别的研究领域的使用和进一步发展。