基于改进YOLOv3网络的烟梗识别定位方法

目的：实现烟叶分级流程烟梗部位的智能抓取，防止智能烟叶分级系统中机械手在抓取烟叶时对叶面造成损伤，减少烟叶智能分级设备生产中的人为操作，解决烟叶分级系统中的单片烟叶识别分类问题与对应等级单片烟叶存放问题。方法：提出一种基于改进YOLOv3的卷积神经网络烟梗自动识别定位模型。该模型在原有的YOLOv3的基础模型上改变单元模块结构引入注意力机制模块，优化模型参数，使用Swish激活函数，实现了对烟叶图像全部信息进行目标定位识别，构建烟梗目标检测模型。结果：改进后的YOLOv3模型的loss能更快的收敛，其mAP由90.46%提升为97.48%，准确率由95.33%提升为97.35%，回归率由84.65%提升为95.65%,为后续烟叶自动化分类打下基础。结论：与YOLOv3、Faster-rcnn、YOLOv4、Efficientdet算法作对比分析表明试验提出的算法更加轻量化，识别效果更好，能减少对烟梗试验平台的硬件配置要求，提高烟叶分类系统的经济效益，为烟叶分级系统中烟叶上料与分仓提供准确的位置信息。

Cigarette stem identification and location method based on improved YOLOv3 network

Objective:In order to realize the intelligent grasping of tobacco stem in tobacco grading process, prevent the manipulator in the intelligent tobacco grading system from damaging the leaf surface during grasping tobacco leaves, and reduce the manual operation in the production of intelligent tobacco grading equipment.Methods:An automatic tobacco stem identification and location model based on improved YOLOv3 convolution neural network was proposed for the identification and classification of single tobacco leaf and the storage of corresponding single tobacco leaf in tobacco grading system. The model changed the structure of the unit module and introduced the attention mechanism module based on the original YOLOv3 model, which optimized the model parameters and used swish activation function to realize the target location and recognition of all the information of tobacco leaf images, and then the tobacco stem target detection model was constructed.Results:The results showed that the loss of improved YOLOv3 model could converge faster, with its mAP increased from 90.46% to 97.48% and its accuracy increased from 95.33% to 97.35%; its regression rate increased from 84.65% to 95.65%, which laid the foundation for the automatic classification of tobacco leaves.Conclusion:Compared with YOLOv3, Faster-rcnn, YOLOv4, Efficientdet algorithm, the proposed algorithm is lighter and more effective. It can reduce the hardware configuration requirements of tobacco stem test platform, improve the economic benefits of tobacco classification system, and provide accurate location information for tobacco feeding and storehouse separation in tobacco classification system.