关键词: 烟丝组分, ShuffleNetV2, BiFPN特征融合机制, CA注意力机制, 改进的YOLOv11

Abstract: In the tobacco manufacturing industry, the accurate classification and composition determination of stem, leaf, expanded and recycled tobacco are the key steps in the formulation of tobacco fiber mixing ratio. The accuracy of this process is directly related to the final quality of the cigarette product, and tobacco is a major challenge in identification detection due to its small size and morphological diversity. At present, there are many problems in the field of tobacco component detection, such as low efficiency and low accuracy. In view of this, this study took YOLOv11 model as the basic detection framework, introduced lightweight ShuffleNetV2 as the network backbone, and integrated BiFPN feature fusion mechanism and CA attention mechanism, and proposed a tobacco type recognition and detection method based on the optimized lightweight YOLOv11 model. The experimental results show that the improved YOLOv11 network model can accurately identify four types of stem, leaf, expanded and recycled tobacco while maintaining the advantages of lightweight model. Specifically, the model has achieved excellent performance of 95.8%、87.5%、87.3% and 81.3% in accuracy rate, recall rate, mAP@.5 and mAP@.5:.95 evaluation indexes, respectively. The process of tobacco type identification and detection proposed in this study not only provides a novel and effective solution for component detection in cigarette production practice, but also explores a potential new path for other product type identification and classification tasks.

Key words: Tobacco components, ShuffleNetV2, BiFPN feature fusion mechanism, CA attention mechanism, Improved YOLOv11