综述与专题评论

机器视觉技术在食品检测中的应用研究进展

  • 何晴 ,
  • 孙莹莹 ,
  • 刘文秋 ,
  • 高丽茵 ,
  • 李丰勇 ,
  • 张婵
展开
  • 1(华测检测认证集团股份有限公司,广东 深圳,518101)
    2(中国科学院深圳先进技术研究院,广东 深圳,518055)
第一作者:博士(刘文秋高级工程师为通信作者,E-mail:liuwenqiu@cti-cert.com)

收稿日期: 2025-06-10

  修回日期: 2025-08-21

  网络出版日期: 2026-05-07

基金资助

深圳市高新区发展专项创新平台建设项目(深科技创新资〔2023〕95号);国家重点研发计划资助项目(2018YFB1403300)

Research progress on application of machine vision technology in food inspection

  • HE Qing ,
  • SUN Yingying ,
  • LIU Wenqiu ,
  • GAO Liyin ,
  • LI Fengyong ,
  • ZHANG Chan
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  • 1(Research Institute, Centre Testing International Group Co.Ltd., Shenzhen 518101, China)
    2(The Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

Received date: 2025-06-10

  Revised date: 2025-08-21

  Online published: 2026-05-07

摘要

相较于传统的人工感官方法,机器视觉在食品外观检测中具有更高的效率、客观性和重复性。随着图像处理技术与人工智能的发展,机器视觉系统不断优化升级,并通过与光谱、电子鼻等技术融合,显著提升了检测效率、准确性和鲁棒性。综述分别介绍了独立机器视觉系统与多源融合体系的基本组成、关键技术原理及先进图像处理算法。同时,综述还归纳总结了2种机器视觉系统在食品品质分级、新鲜度判定及掺假识别等方面的典型应用实例,并进一步剖析了当前存在的关键技术挑战如模型泛化能力不足、多模态数据融合复杂性等问题,最后展望了未来技术发展方向。综述旨在为相关研究人员和行业实践者提供系统参考,促进机器视觉技术在食品检测领域的深入研究与广泛应用。

本文引用格式

何晴 , 孙莹莹 , 刘文秋 , 高丽茵 , 李丰勇 , 张婵 . 机器视觉技术在食品检测中的应用研究进展[J]. 食品与发酵工业, 2026 , 52(8) : 411 -419 . DOI: 10.13995/j.cnki.11-1802/ts.043465

Abstract

Compared with traditional manual sensory methods, machine vision offers higher efficiency, objectivity, and repeatability in food appearance inspection.With the advancement of image processing technologies and artificial intelligence, machine vision systems have been continuously optimized and upgraded.By integrating with technologies such as spectroscopy and electronic nose, these systems have significantly improved detection efficiency, accuracy, and robustness.This review introduces the fundamental components, key technical principles, and advanced image processing algorithm of both standalone machine vision systems and multi-source fusion frameworks.It also summarizes typical application cases of the two types of systems in areas such as food quality grading, freshness assessment, and adulteration detection.Furthermore, the review analyzes current key technical challenges, including limited model generalization and the complexity of multimodal data fusion, and finally outlines future development directions.The aim is to provide systematic references for researchers and industry practitioners, promoting in-depth research and widespread application of machine vision technologies in the field of food inspection.

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