中国烟草学报2025，Vol.31Issue(3)：78-87,10.DOI:10.16472/j.chinatobacco.2024.T0192
基于改进YOLOv5s的异常烟丝识别检测轻量化算法
Lightweight algorithm for improved YOLOv5s-based abnormal cut tobacco detection and recognition
摘要
Abstract
[Objective]On-line detection of cut tobacco quality is a key quality index in cigarette processing,which directly affects the quality of finished products.Aiming at the problems of similar background color,irregular target size,small target susceptible to background interference,and large model calculation in abnormal shape cut tobacco detection,an abnormal cut tobacco detection method based on CBS-YOLOv5s is proposed.[Methods]Aiming at the abnormal cut tobacco of different scales in complex background,the BiFormer attention mechanism is introduced into the neck network to enhance the extraction ability of small target features in complex background.Secondly,the C3-Faster module,which combines partial convolution and point convolution,is used to reduce computational complexity and the number of parameters while ensuring the model's accuracy.Finally,the Shape-IoU function is introduced to further improve the regression accuracy.[Results]The average accuracy of the model established in this study in target detection reached 96.4％,which was 2.5％higher than that of the original model.Compared with Faster R-CNN,YOLOv4-tiny,YOLOv8s,and other models,it increased by 14.8％,25.1％and 1.58％,respectively.In the counting task,it can more effectively analyze the fluctuation of abnormal tobacco.[Conclusion]This study provides an on-line detection method for the stability control and optimization of cutting quality,which is beneficial for promoting the modernization of the silk-making process.关键词
切丝质量/异常检测/YOLOv5s/BiFormer/Shape-IoUKey words
cutting quality/anomaly detection/YOLOv5s/BiFormer/Shape-IoU引用本文复制引用
胡东辉,李嘉康,刘振宇,林苗俏,付主木,李珮珺,魏海锋,张二强,徐大勇,堵劲松..基于改进YOLOv5s的异常烟丝识别检测轻量化算法[J].中国烟草学报,2025,31(3):78-87,10.基金项目
中国烟草总公司科技项目"梗签形成机制及大工艺协同控制技术研究与应用"(110202202010) （110202202010）
