摘要
目的:解决白酒传统摘酒方法“看花摘酒”的主观性和不稳定性,以及现有机器视觉酒花分类方法难以满足实时分类的问题。方法:轻量型YOLOv5以YOLOv5s作为初始模型,使用K-mean聚类的锚框取代默认锚框,以提高模型检测精度和稳定性,使用ShuffleNetV2网络替换YOLOv5s主干网络进行特征提取,以达到轻量化模型的目的,并增加CBAM注意力机制使模型更加关注酒花特征。结果:与YOLOv5s初始模型相比,轻量型YOLOv5模型占用内存减少92.5%,参数量减少93.7%,计算量降低63.4%,检测精度提升2.8%,FPS高达526。结论:轻量型YOLOv5降低了对硬件配置的要求,可以很好地实现酒花实时检测分类。
Objective:To solve the subjectivity and instability of the traditional Baijiu picking method"liquor-receiving according to liquor hop",and the problem that the existing machine vision hops classification method is difficult to meet the real-time classification.Methods:The lightweight YOLOv5 takes YOLOv5 s as the initial model,uses the K-mean clustering anchor box to replace the default anchor box to improve the model detection accuracy and stability,uses the shufflenetv2 network to replace the YOLOv5 s backbone network for feature extraction,so as to achieve the purpose of lightweight model,and adds the CBAM attention mechanism to make the model pay more attention to the characteristics of hops.Results:Compared with the initial YOLOv5 s model,the memory occupied by the lightweight YOLOv5 model is reduced by 92.5%,the parameters are reduced by 93.7%,the calculation is reduced by 63.4%,the detection accuracy is improved by 2.8%,and the FPS is up to 526.Conclusion:The lightweight YOLOv5 reduces the requirements for hardware configuration and can well realize the real-time detection and classification of hops.
作者
刘智萍
崔克彬
LIU Zhi-ping;CUI Ke-bin(Computer Department,North China Electric Power University,Baoding,Hebei 071003,China)
出处
《食品与机械》
北大核心
2022年第11期111-116,共6页
Food and Machinery
基金
河北省自然基金研究项目(编号:F2018502080)。
