食品与发酵工业 >

2025 , Vol. 51 >Issue 17: 143 - 149

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.042871

研究报告

基于深度学习的五粮液包包曲分级框架

  • 赵鹏举 ,
  • 卢彦坪 ,
  • 苏建 ,
  • 赵东 ,
  • 雷学俊 ,
  • 罗青春 ,
  • 李僖 ,
  • 杨皓 ,
  • 汪兴明 ,
  • 张柱 ,
  • 代松林 ,
  • 郑佳
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  • 1(宜宾五粮液股份有限公司,四川 宜宾,644000)
    2(固态发酵资源利用四川省重点实验室,四川 宜宾,644000)
    3(中国轻工业浓香型白酒固态发酵重点实验室,四川 宜宾,644000)
第一作者:博士(郑佳正高级工程师为通信作者, E-mail:zhengwanqi86@163.com)

收稿日期: 2025-03-28

  修回日期: 2025-04-15

  网络出版日期: 2025-09-29

基金资助

四川省中央引导地方科技发展专项(2024ZYD0301)

收起

Deep learning-based grading framework for Wuliangye Baobaoqu

  • ZHAO Pengju ,
  • LU Yanping ,
  • SU Jian ,
  • ZHAO Dong ,
  • LEI Xuejun ,
  • LUO Qingchun ,
  • LI Xi ,
  • YANG Hao ,
  • WANG Xingming ,
  • ZHANG Zhu ,
  • DAI Songlin ,
  • ZHENG Jia
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  • 1(Yibin Wuliangye Co. Ltd., Yibin 644000, China)
    2(Solid-state Fermentation Resource Utilization Key Laboratory of Sichuan Province, Yibin 644000, China)
    3(Key Laboratory of Wuliangye-flavor Liquor Solid-state Fermentation, China National Light Industry, Yibin 644000, China)

Received date: 2025-03-28

  Revised date: 2025-04-15

  Online published: 2025-09-29

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摘要

包包曲是浓香型白酒五粮液在酿造过程中至关重要的发酵原料,其精准分级对于稳定酿酒质量和提高生产效率具有重要作用。然而,传统的包包曲分级方法主要依赖于人工经验,效率较低,且培养一位经验丰富的工程师耗时较长,导致分级结果易受人为因素影响,鲁棒性较差。该研究提出了一种基于深度学习的五粮液包包曲分级框架,包含包包曲截面识别与分级判别2个核心模块,旨在实现分级过程的数字化与高效化,从而辅助工程师提升分级准确率与效率。针对传统人工检测方法的局限性,该研究建立了包包曲图像数据集,并构建了两阶段深度神经网络框架:第一阶段通过目标检测模型识别包包曲截面,第二阶段利用图像分类模型进行分级判别。实验结果表明,该自动化方法的分类性能可媲美经验丰富的工程师,为白酒酿造工业的智能化发展提供了技术支持。

关键词： 浓香型白酒; 包包曲; 截面识别; 包包曲分级; 深度学习; 计算机视觉

本文引用格式

赵鹏举 , 卢彦坪 , 苏建 , 赵东 , 雷学俊 , 罗青春 , 李僖 , 杨皓 , 汪兴明 , 张柱 , 代松林 , 郑佳 . 基于深度学习的五粮液包包曲分级框架[J]. 食品与发酵工业, 2025 , 51(17) : 143 -149 . DOI: 10.13995/j.cnki.11-1802/ts.042871

Abstract

Baobaoqu is a crucial fermentation material in the brewing process of Nongxiangxing Baijiu Wuliangye.Its precise grading plays a significant role in stabilizing brewing quality and improving production efficiency.However, traditional grading methods mainly depend on human expertise, resulting in low efficiency and long training periods to develop experienced engineers.This reliance introduces subjectivity and compromises the robustness of grading outcomes.This study presents a deep learning-based grading framework for Wuliangye Baobaoqu, comprising two core components:cross-section detection and classification.The framework is designed to digitize and streamline the grading process, supporting engineers in improving grading precision and operational efficiency.To overcome the limitations of manual assessment, a dedicated Baobaoqu image dataset was constructed, and a two-stage deep neural network framework was developed.The first stage utilizes an object detection model to identify Baobaoqu cross-sections, and the second stage employs an image classification model for grading decisions.Experimental results demonstrate that the classification performance of the proposed method is comparable to that of experienced engineers, providing technical support for the intelligent development of the Baijiu brewing industry.

Key words： Nongxiangxing Baijiu; Baobaoqu; cross-section recognition; Baobaoqu grading; deep learning; computer vision

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