染整技术2026，Vol.48Issue(2)：39-41,3.
基于机器视觉的纺织面料瑕疵自动检测与分类系统
Automated defect detection and classification system for textile fabrics based on machine vision
摘要
Abstract
In the advancement of intelligent manufacturing within the textile printing and dyeing industry,automation upgrade of fabric quality inspection has become a critical step for enhancing production efficiency and improving product quality.This paper presents a system leveraging machine vision and deep learning technologies to achieve automated defect detection and classification in textile fabrics.A high-resolution line-scan imaging platform suitable for high-speed production lines was designed,employing a multi-angle combined light source configuration to overcome imaging challenges posed by the complex textures of dyed and finished fabrics.At the algorithmic level,an enhanced convolutional neural network model performs deep feature extraction on captured images.Precise segmentation of defect areas is achieved by taking texture suppression algorithms..Additionally,a sample augmentation strategy overcomes classification accuracy limitations caused by imbalanced defect sample data in industrial settings.关键词
机器视觉/面料瑕疵检测/深度学习/线阵相机/自动分类Key words
machine vision/fabric defect detection/deep learning/line-scan camera/automatic classification分类
轻工纺织引用本文复制引用
程翠玉,郑明言,焦峰亮..基于机器视觉的纺织面料瑕疵自动检测与分类系统[J].染整技术,2026,48(2):39-41,3.基金项目
山东省教育厅职业教育教学改革研究项目"基于知识图谱的职业教育精准化教学实施策略研究与实践"(2023341) （2023341）
山东省教育厅职业教育教学改革研究项目"基于VR技术的工科专业数字化转型升级研究与实践"(2024551) （2024551）
山东省教育科学研究院教学研究课题"数智时代知识图谱赋能计算机专业精准化教学研究与实践"(2024JXY544 （）
) （）
山东省高等教育学会教学研究重点课题"基于知识图谱技术的平面设计一流核心课程建设实践研究"(SDGJ2025E10)研究成果 （SDGJ2025E10）
