Particle field measurement is an important topic in many industrial branches. However, there are always complex imaging scenes in the engineering experiments, resulting in severe imaging artifact, noise, and blur, such as the optical holography. In this article, we propose a primary-auxiliary coupled neural network (PANet) for 3-D holographic particle field characterization, which can obtain a comprehensive particle measurement, including the identification, focus determination, segmentation, and size estimation. PANet is constituted by two subnets that are arranged in a coupled architecture, i.e., a Primary-Net (PNet) and an AuxiliaryNet (ANet). As the main frame, PNet is designed to accomplish the detection of most particles, while ANet aims to detect the tiny particles that PNet cannot identify. We exploit an alternative training method to realize their functional differentiation and complementation. PANet is evaluated on two kinds of holographic particle field data, i.e., high-energy laser shock aluminum target and droplet breakup in high Mach shock wave. By means of semisupervised learning and a specific loss function, the effect of deficient particle labeling can be alleviated. Experimental results demonstrate that PANet can achieve excellent performance in particle field characterization, especially for those with a wide size span and complex image background.

中文翻译：

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用于三维全息粒子场表征的主辅耦合神经网络


颗粒场测量是许多工业领域的一个重要课题。然而，工程实验中往往存在复杂的成像场景，导致严重的成像伪影、噪声和模糊，例如光学全息。在本文中，我们提出了一种用于 3D 全息粒子场表征的主辅助耦合神经网络（PANet），它可以获得全面的粒子测量，包括识别、焦点确定、分割和尺寸估计。 PANet由两个以耦合架构排列的子网构成，即主网络（PNet）和辅助网络（ANet）。作为主框架，PNet 旨在完成大多数粒子的检测，而 ANet 旨在检测 PNet 无法识别的微小粒子。我们利用另一种训练方法来实现它们的功能差异化和互补性。 PANet 在两种全息粒子场数据上进行评估，即高能激光冲击铝靶和高马赫冲击波中的液滴破碎。通过半监督学习和特定的损失函数，可以减轻粒子标记缺陷的影响。实验结果表明，PANet 在粒子场表征方面可以取得优异的性能，特别是对于尺寸跨度大和图像背景复杂的粒子场表征。