In-line image analysis of particulate processes with deep learning: Optimizing training data generation via copy-paste augmentation

Daus, Sarah; Buchwald, Thomas; Peuker, Urs A.

doi:10.1016/j.powtec.2024.119884

Powder Technology

2024

DOI: 10.1016/j.powtec.2024.119884

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In-line image analysis of particulate processes with deep learning: Optimizing training data generation via copy-paste augmentation

Sarah Daus,

Thomas Buchwald,

Urs A. Peuker

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2024

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Deep Learning-Based In Situ Micrograph Synthesis and Augmentation for Crystallization Process Image Analysis

Li,

Yao,

Liu

et al. 2024

Mathematics

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Deep learning-based in situ imaging and analysis for crystallization process are essential for optimizing product qualities, reducing experimental costs through real-time monitoring, and controlling the process. However, large and high-quality annotated datasets are required to train accurate models, which are time consuming. Therefore, we proposed a novel methodology that applied image synthesis neural networks to generate virtual information-rich images, enabling efficient and rapid dataset expansion while simultaneously reducing annotation costs. Experiments were conducted on the L-alanine crystallization process to obtain process images and to validate the proposed workflow. The proposed method, aided by interpolation augmentation and data warping augmentation to enhance data richness, utilized only 25% of the training annotations, consistently segmenting crystallization process images comparable to those models utilizing 100% of the training data annotations, achieving an average precision of nearly 98%. Additionally, based on the analysis of Kullback–Leibler divergence, the proposed method demonstrated excellent performance in extracting in situ information regarding aspect ratios and crystal size distributions during the crystallization process. Moreover, its ability to leverage expert labels with a four-fold enhanced efficiency holds great potential for advancing various applications in crystallization processes.

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Deep Learning-Based In Situ Micrograph Synthesis and Augmentation for Crystallization Process Image Analysis

Li,

Yao,

Liu

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

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In-line image analysis of particulate processes with deep learning: Optimizing training data generation via copy-paste augmentation

Cited by 1 publication

References 40 publications

Deep Learning-Based In Situ Micrograph Synthesis and Augmentation for Crystallization Process Image Analysis

Deep Learning-Based In Situ Micrograph Synthesis and Augmentation for Crystallization Process Image Analysis

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