基于YOLOv4的烟叶烟梗识别算法研究
Research on Tobacco Stem Recognition Algorithm Based on YOLOv4
张江涛 1王堃阳1
作者信息
- 1. 华北水利水电大学,河南 郑州 450045
- 折叠
摘要
针对烟叶分级系统中烟叶上料、下料中的烟梗识别定位问题,提出了一种新型烟梗检测算法:基于YOLOv4卷积神经网络,将其主干网络修改替换为Efficientdet,降低网络模型的体积并提高模型对烟梗类型的识别准确率,在此基础上引入深度可分离卷积,并在深度可分离卷积中引入宽度因子alpha变量,优化了系统参数,简化了网络结构.结果表明,此算法在单姿态烟梗识别中其mAP值均优于SSD、Centernet、Faster-rcnn、YOLOv4little以及YOLOv4这几种目标检测算法,在多姿态烟梗识别中其mAP值和YOLOv4算法相差仅为2%,可减少网络参数,提高模型检测准确率与系统识别速度.
Abstract
In response to the problem of identifying and locating tobacco stems during tobacco loading and unloading in tobacco grading systems,this paper proposed a YOLOv4 convolutional neural network.Based on this,depth separable convolution was intro-duced,and the width factor alpha variable was introduced in depth separable convolution to optimize system parameters and simpli-fied network structure.The experimental results showed that the mAP value of the algorithm proposed in this paper was superior to several object detection algorithms such as SSD,Centernet,Faster-rcnn,YOLOv4little,and YOLOv4 in single pose tobacco stem rec-ognition.In multi pose tobacco stem recognition,the difference in mAP value between the algorithm and YOLOv4 algorithm is only 2%,but it can significantly reduce network parameters,improve model detection accuracy and system recognition speed.
关键词
烟梗识别定位/YOLOv4/EfficientNet/深度可分离卷积Key words
tobacco stem recognition and localization/YOLOv4/EfficientNet/depthwise separable convolution引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据