基于改进YOLOv8n的轻量化织物疵点检测算法  

Fabric Defect Detection Based on Improved Lightweight YOLOv8n

作　　者：刘玉娜 马双宝 LIU Yuna;MA Shuangbao(School of Mechanical Engineering and Automation,Wuhan Textile University,Wuhan Hubei 430200,China;Hubei Province Digital Textile Equipment Laboratory(Wuhan Textile University),Wuhan Hubei 430200,China)

机构地区：[1]武汉纺织大学机械工程与自动化学院,湖北武汉430200 [2]湖北省数字化纺织装备重点实验室(武汉纺织大学),湖北武汉430200

出　　处：《广西师范大学学报(自然科学版)》2025年第2期83-94,共12页Journal of Guangxi Normal University:Natural Science Edition

基　　金：国家自然科学基金(62103309);湖北省数字化纺织装备重点实验室公开项目(DTL2022007)。

摘　　要：为应对织物疵点目标检测中背景纹理复杂以及硬件资源有限问题,本文提出一种基于改进YOLOv8n的轻量化织物疵点检测算法(GSL-YOLOv8n)。首先,为减少YOLOv8n模型参数量与网络结构复杂度,结合Ghost思想构建C2fGhost模块,并用Ghost卷积层替换YOLOv8n网络结构的普通卷积(Conv);其次,在主干网络末端嵌入无参注意力机制SimAM,去除冗余背景,增强小目标语义信息和全局信息,增强网络特征提取能力;最后,设计轻量化共享卷积检测头LSCDH,运用Scale层对特征进行缩放,在保证模型轻量化的同时尽可能减少精度损失。改进后的算法GSL-YOLOv8n相比原YOLOv8n模型平均精度提升0.60%,达到98.29%,检测速度FPS基本保持不变,模型体积、计算量和参数量分别减少66.7%、58.0%和67.4%,满足纺织工业生产对织物疵点检测的应用要求。In order to address the challenges of complex background textures,and limited hardware resources in fabric defect detection,a lightweight fabric defect detection method based on improved YOLOv8n(GSL-YOLOv8n)is proposed.Firstly,to reduce the parameter count and complexity of the YOLOv8n model,a C2f Ghost module is constructed based on the Ghost idea and utilized to replace the regular convolutions(Conv)in the YOLOv8n network structure.Secondly,a parameter-free attention mechanism,SimAM,is embedded at the end of the backbone network to remove redundant background,enhance semantic information of small targets,and improve global information,enhancing the network’s feature extraction capability.Finally,a lightweight shared convolutional detection head(LSCDH)is designed to scale the features using a Scale layer,minimizing accuracy loss while ensuring model lightweightness.Compared with the original YOLOv8n model,the improved algorithm GSL-YOLOv8n achieves an average precision improvement of 0.60%,reaching 98.29%,and the detection speed FPS remains basically the same.The model size,computational complexity,and parameter count are reduced by 66.7%,58.0%,and 67.4%respectively,meeting the application requirements of fabric defect detection in the textile industry.

关 键 词：织物疵点 YOLOv8 GhostNet 注意力机制 轻量化 目标检测 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]