Accurate real-time monitoring of particulate matter (PM) has emerged as a global issue due to the hazardous effects of PM on public health and industry. However, conventional PM monitoring techniques are usually cumbersome and require expensive equipment. In this study, Holo-SpeckleNet is proposed as a fast and accurate PM concentration measurement technique with high throughput using a deep learning based holographic speckle pattern analysis. Speckle pattern datasets of PMs for a wide range of PM concentrations were acquired by using a digital in-line holography microscopy system. Deep autoencoder and regression algorithms were trained with the captured speckle pattern datasets to directly measure PM concentration from speckle pattern images without any air intake device and time-consuming post image processing. The proposed technique was applied to predict various PM concentrations using the test datasets, optimize hyperparameters, and compare its performance with a convolutional neural network (CNN) algorithm. As a result, high PM concentration values can be measured over air quality index of 150, above which human exposure is unhealthy. In addition, the proposed technique exhibits higher measurement accuracy and less overfitting than the CNN with a relative error of 7.46 ± 3.92%. It can be applied to design a compact air quality monitoring device for highly accurate and real-time measurement of PM concentrations under hazardous environment, such as factories or construction sites.

由于颗粒物对公共卫生和工业的有害影响，精确实时的颗粒物（PM）监测已成为全球性问题。然而，常规的PM监测技术通常很麻烦并且需要昂贵的设备。在这项研究中，Holo-SpeckleNet被提出为一种快速，准确的PM浓度测量技术，使用基于深度学习的全息散斑图分析进行高通量测量。通过使用数字在线全息显微系统，获得了多种PM浓度的PM散斑图数据集。使用捕获的散斑图数据集训练深度自动编码器和回归算法，以直接从散斑图图像中测量PM浓度，而无需任何进气装置和费时的后期图像处理。所提出的技术用于使用测试数据集预测各种PM浓度，优化超参数，并将其性能与卷积神经网络（CNN）算法进行比较。结果，可以在空气质量指数为150的情况下测量到较高的PM浓度值，超过该值人体暴露是不健康的。另外，所提出的技术比CNN具有更高的测量精度和更少的过拟合，相对误差为7.46±3.92％。它可用于设计紧凑型空气质量监测设备，以在工厂或建筑工地等危险环境下高精度，实时地测量PM浓度。结果，可以在空气质量指数为150的情况下测量到较高的PM浓度值，超过该值人体暴露是不健康的。另外，所提出的技术比CNN具有更高的测量精度和更少的过拟合，相对误差为7.46±3.92％。它可用于设计紧凑型空气质量监测设备，以在工厂或建筑工地等危险环境下高精度，实时地测量PM浓度。结果，可以在空气质量指数为150的情况下测量到较高的PM浓度值，超过该值人体暴露是不健康的。另外，所提出的技术比CNN具有更高的测量精度和更少的过拟合，相对误差为7.46±3.92％。它可用于设计紧凑型空气质量监测设备，以在工厂或建筑工地等危险环境下高精度，实时地测量PM浓度。