烟草科技2025，Vol.58Issue(10)：103-112,10.DOI:10.16135/j.issn1002-0861.2025.0020
基于轻量化语义分割模型的再造烟叶表面缺陷检测方法
Surface defect detection for reconstituted tobacco based on a lightweight semantic segmentation model
摘要
Abstract
To address the issue of low detection efficiency of surface defects for reconstituted tobacco,a detection method based on a lightweight semantic segmentation model was proposed and evaluated.Firstly,reconstituted tobacco samples were batch processed to construct a sample dataset.Secondly,the Labelme software was used to label and classify the defects,and sufficient model training samples were obtained through the sliding window sampling method.Finally,the lightweight semantic segmentation model Deeplab-T was constructed using MobileNetV2 and the void convolution as the backbone network,and the convolutional block attention module was introduced in the feature fusion process to highlight the segmentation target.The results showed that:1)The mean intersection over union and mean pixel accuracy of Deeplab-T model were 76.35%and 83.71%,respectively,which were 13.98 and 15.32 percentage points higher than those of the DeeplabV3+model.Additionally,the detection frames per second was increased by 234.10%,meeting the industrial requirements for both accuracy and speed.2)The Deeplab-T model achieved the contour segmentation of reconstituted tobacco defects in different directions,proportions and of different types with good robustness.This study provides a theoretical reference for the online monitoring of typical defects in reconstituted base sheet making process.关键词
再造烟叶/深度学习/语义分割/CBAM注意力机制/表面缺陷检测Key words
Reconstituted tobacco/Deep learning/Semantic segmentation/CBAM attention mechanism/Surface defect detection分类
轻工业引用本文复制引用
王水明,吴千旭,李鹏飞,黄文,陈前进,段州君,彭瑞,舒衡,魏宁丰,张涛..基于轻量化语义分割模型的再造烟叶表面缺陷检测方法[J].烟草科技,2025,58(10):103-112,10.基金项目
湖北省自然科学基金面上项目"融合结构和多传感数据的微流道增材制造缺陷监测方法研究"(2023AFB878) （2023AFB878）
湖北省自然科学基金青年项目"可调环形光斑对铝合金薄壁构件激光焊接飞溅的抑制机理与工艺调控"(2024AFB259). （2024AFB259）
