合肥工业大学校徽

合肥工业大学学报自科版

合肥工业大学学报(自然科学版)

JOURNAL OF HEFEI UNIVERSITY OF TECHNOLOGY (NATURAL SCIENCE)

主管：中华人民共和国教育部

主办：合肥工业大学

ISSN 1003-5060, CN 34-1083/N

基于改进 YOLO v4 的轻量化烟梗识别方法

Lightweight tobacco stem identification method based on improved YOLO v4

期刊信息

合肥工业大学（自然科学版），2023年9月，第46卷第9期：1196-1202，1253

DOI: 10.3969/j.issn.1003-5060.2023.09.008

作者信息

郑银环，林晓琛，吴飞，金圣洁，吴傲男

（武汉理工大学机电工程学院，湖北武汉430070）

摘要和关键词

摘要: 为完成烟叶精选工艺流程中打叶复烤后破碎烟叶的进一步去梗,实现破碎烟叶中烟梗的自动化检测,文章提出基于改进YOLO v4的轻量化烟梗识别方法。在YOLO v4基础模型上先后进行通道剪枝和层剪枝,大幅简化模型结构,改进后模型存储空间下降了93.77%,模型平均精度均值(mean average precision, mAP)和前向运算时间与基础模型持平。与同类别算法相比,模型精度平均提升8.7%,模型参数量大幅缩减。实验结果表明该实验剪枝模型更具轻量化,识别效果更好,能够满足实际生产需求。

关键词: 烟梗识别；YOLO v4；通道剪枝；层剪枝；轻量化

Authors

ZHENG Yinhuan, LIN Xiaochen, WU Fei, JIN Shengjie, WU Aonan

(School of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan 430070, China)

Abstract and Keywords

Abstract: In order to realize further stem removal of broken tobacco leaves after leaf beating and re-roasting in tobacco leaf selection process and realize automatic detection of tobacco stems in broken tobacco leaves, a lightweight tobacco stem identification method based on improved YOLO v4 was proposed. In YOLO V4 model, channel pruning and layer pruning were carried out successively to simplify the model structure greatly. The storage space of the improved model is reduced by 93.77%, and the mean average precision (mAP) of the model and the forward computing time are the same as those of the basic model. Compared with algorithms of the same category, the model accuracy is improved by 8.7% on average, and the number of model parameters is greatly reduced. The analysis shows that the pruning model is more lightweight, has better identification effect, and meets the actual production demand.

Keywords: tobacco stem identification; YOLO v4; channel pruning; layer pruning; lightweight

基金信息

国家自然科学基金资助项目（52005376）

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