基于深度学习的白酒酒花实时分类方法
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YOLOv5 s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YOLOv5 s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YOLOv5 s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...
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| Published in | 食品与机械 Vol. 38; no. 11; pp. 111 - 116 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | Chinese |
| Published |
华北电力大学计算机系,河北 保定 071003
2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1003-5788 |
| DOI | 10.13652/j.spjx.1003.5788.2022.80168 |
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| Abstract | 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YOLOv5 s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YOLOv5 s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YOLOv5 s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. |
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| AbstractList | 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YOLOv5 s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YOLOv5 s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YOLOv5 s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. |
| Author | 崔克彬 刘智萍 |
| AuthorAffiliation | 华北电力大学计算机系,河北 保定 071003 |
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| Author_FL | LIU Zhi-ping CUI Ke-bin |
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| Author_xml | – sequence: 1 fullname: 刘智萍 – sequence: 2 fullname: 崔克彬 |
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| Keywords | 实时分类 ShuffleNetV2 白酒酒花 YOLOv5 CBAM注意力机制 |
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