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。
In order to realize further stem removal of broken tobacco leaves after leaf beating and reroasting 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.
作者
郑银环
林晓琛
吴飞
金圣洁
吴傲男
ZHENG Yinhuan;LIN Xiaochen;WU Fei;JIN Shengjie;WU Aonan(School of Mechanical and Electrical Engineering,Wuhan University of Technology,Wuhan 430070,China)
出处
《合肥工业大学学报(自然科学版)》
CAS
北大核心
2023年第9期1196-1202,1253,共8页
Journal of Hefei University of Technology:Natural Science
基金
国家自然科学基金资助项目(52005376)。
关键词
烟梗识别
YOLO
v4
通道剪枝
层剪枝
轻量化
tobacco stem identification
YOLO v4
channel pruning
layer pruning
lightweight