中国烟草学报2025，Vol.31Issue(2)：47-57,11.DOI:10.16472/j.chinatobacco.2024.T0168
一种实时高精度烟支外观缺陷检测方法
A real-time high-precision detection model for cigarette appearance defects
摘要
Abstract
[Background]Appearance defects in cigarette production are inevitable during production in cigarette factories,and such defects can severely impact the quality of tobacco products.Therefore,there is a need for real-time detection and removal of defective cigarettes on high-speed production lines.[Methods]This paper proposes a real-time defects detection model for cigarette appearance based on improved YOLOv7tiny,named CAD-YOLO.In this model,the upgraded version of deformable convolutional networks(DCNv2)is introduced in the feature extraction network,which adds offsets to sampling points to flexibly extract features,adapting to complex defect geometries and enhancing the model's feature extraction capability.In the feature fusion pyramid of the neck network,a bidirectional weighted feature pyramid is incorporated,along with a skip connection from the P2 feature layer to the P5 feature layer,which strengthens the feature fusion capability in deep-layer features of the neck network.Moreover,WIoUv3 localization loss function is introduced to reduce adverse gradients from low-quality instances in the dataset,increasing localization accuracy and detection precision..Finally,the Attention-based Intrascale Feature Interaction(AIFI)module,featuring multi-head self-attention,replaces the feature pooling pyramid module,further enhancing multi-scale fusion capabilities.[Results]The experimental results show that the CAD-YOLO model achieves an average detection accuracy of 94.1%,a recall rate of 92.4%,and a detection time of only 12.0ms per cigarette image.[Conclusion]The proposed model can be applied to high-speed cigarette production lines,providing assurance for quality control in cigarette production.关键词
烟支/外观缺陷/目标检测/YOLOv7tiny/深度学习Key words
cigarette/appearance defect/object detection/YOLOv7tiny/deep learning引用本文复制引用
袁国武,马一海,吴昊,袁宝仪,周浩..一种实时高精度烟支外观缺陷检测方法[J].中国烟草学报,2025,31(2):47-57,11.基金项目
云南省科技厅-云南大学"双一流"建设联合专项重点项目"基于深度学习的烟支外观缺陷检测"(No.202201BF070001-005)国家自然科学基金项目"基于深度学习的太阳射电频谱自动检测研究"(No.12263008) （No.202201BF070001-005）
