包装与食品机械2025，Vol.43Issue(2)：30-38,9.DOI:10.3969/j.issn.1005-1295.2025.02.004
TCN-Net:融合三重注意力机制与特征聚焦扩散的烟支缺陷检测网络
TCN-Net:a cigarette defect detection network integrating triplet attention mechanism and feature focus-diffusion
摘要
Abstract
To address the challenges in detecting minute and subtle defects(e.g.,punctures,yellow stains,oil spots,and hidden tobacco shred inclusions)during cigarette production and the limited ability to distinguish between easily confusable defects,this study proposes an improved defect detection network named TCN-Net.The network integrates a triplet attention mechanism to enhance features across channel,height,and width dimensions,significantly improving the detection capability for tiny targets.This innovation increases the mAP@0.5 for puncture defects by 2.4 percentage points.A feature focus-diffusion structure was designed to optimize the fusion of high-level semantic and low-level spatial features,effectively enhancing the differentiation of easily confusable defects(e.g.,oil spots vs.yellow stains),with respective mAP@0.5 improvements of 2.8 and 1.9 percentage points.The normalized Wasserstein distance(NWD)loss function was employed to refine target localization and boost small object detection accuracy.Experimental results demonstrate that compared to the baseline YOLOv8 model,TCN-Net achieves a 5.4 percentage point improvement in mAP@0.5,outperforming mainstream detection algorithms including SSD,YOLOv5,and YOLOv7 in comprehensive performance.This research provides a more precise solution for tobacco industrial defect detection.关键词
图像处理/烟支缺陷检测/深度学习/注意力机制/NWD损失函数Key words
image processing/cigarette defect detection/deep learning/attention mechanism/NWD loss function分类
轻工业引用本文复制引用
吴庆华,张哲铭,赵德华..TCN-Net:融合三重注意力机制与特征聚焦扩散的烟支缺陷检测网络[J].包装与食品机械,2025,43(2):30-38,9.基金项目
国家自然科学基金项目(51275158) （51275158）
湖北省创新群体项目(2022CFA006) （2022CFA006）
