目的 提取烟包图像数据训练深度学习目标检测模型，提升烟包流水线拣包效率和准确性。方法 基于深度学习建立一种烟包识别分类模型，对原始YOLOv3模型进行改进，在原网络中加入设计的多空间金字塔池化结构(M–SPP)，将64×64尺度的特征图下采样与32×32尺度的特征图进行拼接，并去除16×16尺度的预测特征层，提高模型的检测准确率和速度，并采用K–means++算法对先验框参数进行优化。结果 实验表明该目标检测模型平均准确率达到99.68%，检测速度达到70.82帧/s。结论 基于深度学习建立的图像识别分类模型准确率高且检测速度快，有效满足烟包流水线自动化实时检测。

The work aims to extract the cigarette pack image data to train the deep learning target detection model, and improve the efficiency and accuracy of cigarette pack assembly lines. A cigarette pack recognition and classification model was established based on deep learning to improve the original YOLOv3 model. The designed multi-space pyramid pooling structure (M-SPP) was added to the original network. The downsampling of the 64×64 feature map was spliced with that of the 32×32 feature map. The prediction feature layer of 16×16 was removed to improve the detection accuracy and speed of the model, and the K-means++ algorithm was used to optimize the a priori frame parameters. The experiment showed that the average accuracy of the target detection model reached 99.68%, and the detection speed reached 70.82 frames per second. It is concluded that the image recognition and classification model established based on deep learning has high accuracy and fast detection speed, which can effectively meet the automatic real-time detection of cigarette pack assembly lines.