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Multiscale Optical PM2.5 Particles Recognition and Sorting System in Dust Probes

机译:灰尘探测器中的多尺度光学PM2.5颗粒识别和分选系统

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Authors propose the novel approach for optical PM2.5 and PM10 particles recognition and sorting based on Super-resolution neural networks in the research on dust emissions from industrial enterprises. The objective of the dust emissions analysis is to determine their component composition and the fine particle size distribution (PM10 and PM2.5). The scanning electronic microscope of high resolution was used to obtain the set of large-scale images of dust particles. We use the images of particles in different scales as the entire imagery data to create the high quality image suitable for reliable particles recognition and use quality metrics PSNR, MSE, SSIM to ensure that the created image is close to ground truth.
机译:在工业企业粉尘排放的研究中,作者提出了基于超分辨率神经网络的光学PM2.5和PM10颗粒识别和分类的新方法。粉尘排放分析的目的是确定其成分组成和细粒度分布(PM10和PM2.5)。使用高分辨率的扫描电子显微镜获得了尘埃颗粒的大型图像集。我们将不同比例的粒子图像用作整个图像数据,以创建适用于可靠粒子识别的高质量图像,并使用质量指标PSNR,MSE,SSIM来确保所创建的图像接近地面实况。

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