Deep learning has shown recent key breakthroughs in enabling particulate identification directly from scattering patterns. However, moving such a detector from a laboratory to a real-world environment means developing techniques for improving the neural network robustness. Here, a methodology for training data augmentation is proposed that is shown to ensure neural network accuracy, despite occlusion of the scattering pattern by simulated particulates deposited on the detector’s imaging sensor surface. The augmentation approach was shown to increase the accuracy of the network when identifying the geometric Y-dimension of the particulates by ∼62% when 1000 occlusions of size ∼5 pixels were present on the scattering pattern. This capability demonstrates the potential of data augmentation for increasing accuracy and longevity of a particulate detector operating in a real-world environment.

深度学习最近在直接从散射模式中识别微粒方面取得了重大突破。然而，将这样的探测器从实验室转移到现实环境中意味着开发提高神经网络鲁棒性的技术。在这里，提出了一种用于训练数据增强的方法，尽管沉积在探测器成像传感器表面上的模拟颗粒遮挡了散射模式，但该方法可以确保神经网络的准确性。当散射图案上存在 1000 个大小为 5 个像素的遮挡物时，增强方法在识别颗粒的几何 Y 维度时将网络的准确性提高了 62%。