Image data augmentation methods and their applications in process analytical technology

FAN Yunlong; ZHOU Guangcheng; ZHANG Jingkang; SANG Yani; YUAN Yue; WANG Xuezhong

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Image data augmentation methods and their applications in process analytical technology

更新时间：2026-05-09

- Image data augmentation methods and their applications in process analytical technology
- CIESC Journal (2026)
- 作者机构：
  
  北京石油化工学院新材料与化工学院, 恩泽生物质精细化工北京市重点实验室, 北京 102617
- 作者简介：
- 基金信息：
- DOI：
  CLC： TQ 014
- Received：06 February 2026，
  
  Revised：2026-05-09，
  
  Accepted：09 May 2026，
- 稿件说明：
移动端阅览
FAN Yunlong, ZHOU Guangcheng, ZHANG Jingkang, et al. Image data augmentation methods and their applications in process analytical technology[J/OL]. CIESC Journal, 2026.
DOI：

FAN Yunlong, ZHOU Guangcheng, ZHANG Jingkang, et al. Image data augmentation methods and their applications in process analytical technology[J/OL]. CIESC Journal, 2026. DOI：

摘要

随着原位显微成像与工业视觉系统的不断发展，图像分析技术已成为生产过程智能化调控的重要手段。深度学习在图像分析任务中得到广泛应用，但受高质量数据采集成本高、人工标注工作量大等现实问题限制。数据增强技术通过计算机算法扩大数据集规模、丰富样本特征多样性，从而有效提高模型性能。本文对过程分析技术（process analytical technology，PAT）领域常用的各类图像数据增强方法做了系统介绍，包含传统的非深度学习方法和基于深度学习的先进方法，同时重点梳理了它们在结晶、气液多相流、细胞培养等典型应用场景中的研究进展，强调了对物理一致性的相关保障。文章还展望了数据增强在提高在线检测智能化水平、加快工业应用方面的发展方向。

Abstract

With the advance of in situ microscopic imaging and industrial vision systems

image analysis technology has become quite important for the intelligent regulation and control of production processes. Deep learning is increasingly applied to various image analysis tasks

but its implementation is often hindered by practical issues

such as the high cost of acquiring high-quality data and the large manual labor of data annotation. Data augmentation techniques expand the dataset size and enrich the diversity of sample features via computer algorithms

and thus effectively enhance model performance. This paper gives a systematic review of different augmentation strategies for image data in the field of process analytical technology (PAT)

including traditional methods and advanced deep learning methods. Moreover

recent progresses in the applications of those approaches in some typical scenarios are introduced in details

such as crystallization

gas-liquid multiphase flow

and cell culture

while the strategies for maintaining physical consistency are particularly stressed. Finally

some future directions of data augmentation are prospected for enhancing the intelligence level of online monitoring and accelerating its industrial applications.

关键词

Keywords

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