2022
DOI: 10.3390/cryst12121690
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Image Measurement of Crystal Size Growth during Cooling Crystallization Using High-Speed Imaging and a U-Net Network

Abstract: In this paper, an image measurement method using a high-speed imaging system is proposed for the evolution of crystal population sizes during cooling crystallization processes. Firstly, to resist the negative effect from solution stirring and particle motion during crystallization, a U-net network-based image processing method is established to efficiently detect sufficiently clear crystals from the online captured microscopic images. Accordingly, the crystal size distribution model is analyzed in terms of the… Show more

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Cited by 4 publications
(2 citation statements)
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“…In the U-Net model structure, nodes X0.4 only form a skip connection with nodes X0.0, while in the U-Net++ model structure, nodes X0.4 connect the outputs of four convolution units X0.0, X0.1, X0.2, and X0.3 at the same layer, where each node Xi.j represents one convolution down-sampling or deconvolution up-sampling. The U-Net++ network has a nested structure and dense skip paths, which is conducive to aggregating features with different semantic scales on the decoder subnetwork and has achieved excellent performance levels in other fields [12,13]. In this paper, after grayscale processing, the sorbite was quite different from other tissues, which was intuitively applicable to this problem.…”
Section: Network Architecturementioning
confidence: 91%
“…In the U-Net model structure, nodes X0.4 only form a skip connection with nodes X0.0, while in the U-Net++ model structure, nodes X0.4 connect the outputs of four convolution units X0.0, X0.1, X0.2, and X0.3 at the same layer, where each node Xi.j represents one convolution down-sampling or deconvolution up-sampling. The U-Net++ network has a nested structure and dense skip paths, which is conducive to aggregating features with different semantic scales on the decoder subnetwork and has achieved excellent performance levels in other fields [12,13]. In this paper, after grayscale processing, the sorbite was quite different from other tissues, which was intuitively applicable to this problem.…”
Section: Network Architecturementioning
confidence: 91%
“…Building upon FCN, Ronneberger et al [14] established the U-Net model, utilizing a symmetric coding structure combined with feature fusion to enhance segmentation accuracy. U-Net has earned widespread adoption in image segmentation applications [15]. Another notable approach, SegNet [16] was proposed with a coding-decoding structure based on FCN and used atrous convolution and a conditional random field to improve segmentation outcomes.…”
Section: Introductionmentioning
confidence: 99%