文章摘要
刘云飞,杨旭东,孙栋.基于改进YOLOv5的条烟识别研究[J].包装工程,2024,45(5):144-150.
LIU Yunfei,YANG Xudong,SUN Dong.Strip Tobacco Identification Based on Improved YOLOv5[J].Packaging Engineering,2024,45(5):144-150.
基于改进YOLOv5的条烟识别研究
Strip Tobacco Identification Based on Improved YOLOv5
投稿时间:2023-06-20  
DOI:10.19554/j.cnki.1001-3563.2024.05.017
中文关键词: YOLOv5算法  条烟识别  Ghost模块  CA注意力机制
英文关键词: YOLOv5 algorithm  strip tobacco identification  Ghost module  CA attention mechanism
基金项目:贵州省普通高等学校青年科技人才成长项目(黔教合KY字[2021]268);贵州省烟草公司贵阳市公司科技项目(黔烟筑科[2022]1);贵阳市科技计划项目(筑科合同[2022]5-56)
作者单位
刘云飞 贵州大学 机械工程学院贵阳 550025 
杨旭东 贵州大学 机械工程学院贵阳 550025 
孙栋 贵州理工学院 贵阳 550025 
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中文摘要:
      目的 针对当下烟草物流中心条烟分拣机及人工分拣时会产生错烟等问题。从兼顾实时性、识别精度出发,基于YOLOv5s算法提出一种收敛速度更快、准确率更高的条烟识别模型。方法 首先在YOLOv5s网络架构中融入CA注意力模块来更好地提取特征,提高模型获取目标位置的准确度;其次将原网络中的最近邻插值上采样算子改为轻量级通用上采样算子CARAFE,获得更大的感受野;然后在骨干网络中嵌入Ghost模块,对网络进行轻量化处理;最后在烟草物流中心搭建条烟图像采集系统,建立条烟图像数据集。结果 相较于YOLOv5s,本文提出的优化算法计算量减少了45.8%,mAP@0.5值达到了99.3%,在条烟纠错系统上识别率约为99.9%。结论 本文提出的优化算法能够高精度满足高速条烟分拣识别需求。
英文摘要:
      To solve the problems of picking errors of strip tobacco by sorting machine and manual sorting in tobacco logistics center, the work aims to propose a strip tobacco identification model with faster convergence speed and higher accuracy based on YOLOv5s algorithm from the perspective of ensuring the real-time performance and recognition accuracy. Firstly, CA attention module was integrated into the YOLOv5s network architecture to better extract features and improve the accuracy of target acquisition. Then, the nearest neighbor in the original network was changed to lightweight general upper sampling operator CARAFE to obtain a larger feeling field. Next, the Ghost module was embedded in the backbone network to lightweight the network. Finally, the tobacco image acquisition system was built in the tobacco logistics center to establish the tobacco image data set. Compared with YOLOv5s, the proposed optimization algorithm was reduced by 45.8%, mAP@0.5 reached 99.3%, and the recognition rate was about 99.9% on the strip tobacco error correction system. The optimization algorithm proposed can meet the requirements of high-speed strip tobacco sorting and identification with high accuracy.
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