烟草科技2023，Vol.56Issue(12)：76-83,8.DOI:10.16135/j.issn1002-0861.2023.0342
基于物体显著性自监督学习的片烟杂物检测方法
Method for identifying foreign matters in tobacco strips based on object saliency self-supervised learning
王小飞 1李东方 2李玉珩 1陈传通2
作者信息
- 1. 秦皇岛烟草机械有限责任公司,河北省秦皇岛市经济技术开发区龙海道67号 066000
- 2. 山东中烟工业有限责任公司济南卷烟厂,济南市历城区科航路2006号 250100
- 折叠
摘要
Abstract
To identify foreign matters in tobacco strips more accurately,a foreign matter identifying method based on object saliency self-supervised learning was proposed by combining deep learning image processing with clustering algorithm.First,a deep learning network is used to identify salient objects in tobacco strip images,and then clustering analysis is performed on the features of the identified salient objects,and then tobacco strips are removed from the images.Finally,the state accumulation detection method based on time series is used to authenticate the identified foreign matters.The results showed that the average IoU(Intersection Over Union)and MAE(Mean Absolute Error)of the established two-stage U-Net model were 0.90 and 0.054,respectively,which were superior to those of the BASNet and U-Net models.The average identifying accuracy was 96.6%.The time needed for processing a single image was 21 ms,which met the requirements of real-time detection.This method improves the classification and detection efficiency of foreign matters in tobacco strips.关键词
片烟/杂物/识别/深度学习/显著性目标检测/聚类分析Key words
Tobacco strip/Foreign matter/Identification/Deep learning/Salient object detection/Cluster analysis分类
轻工纺织引用本文复制引用
王小飞,李东方,李玉珩,陈传通..基于物体显著性自监督学习的片烟杂物检测方法[J].烟草科技,2023,56(12):76-83,8.
