基于深度学习技术的烟梗形态分类与识别  

Classification and identification of tobacco stem morphology based on deep learning technology

作　　者：肖雷雨 王澍 刘渊根[4] 张龙 王玲 堵劲松[3] 徐大勇[3] XIAO Leiyu;WANG Shu;LIU Yuangen;ZHANG Long;WANG Ling;DU Jinsong;XU Dayong(Institute of Physical Science and Information Technology,Anhui University,Hefei 230601,China;Hefei Institute of Physical Science,CAS,Hefei 230031,China;Zhengzhou Tobacco Research Institute of CNTC,Zhengzhou 450001,China;Anhui Provincial Tobacco Corporation,Hefei 230072,China)

机构地区：[1]安徽大学物质科学与信息技术研究院,合肥市230601 [2]中科院合肥物质科学研究院,合肥市230031 [3]中国烟草总公司郑州烟草研究院,郑州450001 [4]安徽省烟草公司,合肥市230072

出　　处：《烟草科技》2021年第6期65-74,共10页Tobacco Science & Technology

基　　金：中国烟草总公司郑州烟草研究院院长基金项目“基于机器视觉的烟梗形态分类识别技术研究”(212018CA0169)。

摘　　要：为解决打叶复烤中人工分选纯烟梗、梗头及梗含叶检测效率低、识别误差大等问题,基于深度学习方法建立了一种烟梗在线分类识别模型。首先,基于数字图像处理方法对采集到的烟梗图像进行目标提取,制作烟梗数据集;其次,根据烟梗图像特征对原始YOLOv3模型进行改进,构建新的网络结构;然后,使用制作好的烟梗数据对改进后YOLOv3模型进行训练,生成深度学习烟梗分类识别模型;最后,将模型加载于烟梗在线分类识别系统对其性能进行验证。结果表明:所建立模型在测试集上的表现良好,烟梗识别精确度达到95.01%,相比原始YOLOv3模型提高5.97百分点,召回率提高4.76百分点,且均优于SSD与Mask R-CNN等模型;针对不同复杂场景,模型抗干扰能力强,可有效识别出烟梗位置及类别,能够满足烟梗快速分类识别需求。该方法可为提高烟梗分类效率和识别精度提供支持。In order to improve the efficiency and accuracy of identification of tobacco stems(pure stem,stem head and lamina-attached stem)during green leaf threshing,an on-line classification and identification model for tobacco stems was established based on deep learning.First,a digital image processing method was used to extract the target from the collected tobacco stem images and create a tobacco stem data set.Second,the original YOLOv3 model was revised according to the characteristics of tobacco stem images to configure a new network structure.Then,the revised YOLOv3 model was trained with the created data set to establish a deep learning-based classification and identification model for tobacco stems.Finally,the established model was loaded into an on-line tobacco stem classification and identification system to verify its performance.The results showed that the established model performed well on the test set,the identification accuracy of the established model reached 95.01%,it was 5.97 percentage points higher than the original YOLOv3 model,and the recall rate increased by 4.76 percentage points,both were better than those of SSD and Mask R-CNN models.Featuring a strong anti-interference ability under different complex scenarios,the established model is effective to identify the locations and categories of tobacco stems and competent for the rapid classification and identification of tobacco stems.This method provides a technical support for promoting the classification efficiency and identification accuracy of tobacco stems.

关 键 词：打叶复烤 深度学习 烟梗 YOLOv3模型 分类 识别 

分 类 号：TS443[农业科学—烟草工业]