基于视觉特征强化的环锭纺细纱断头 在线检测方法  

Online detection of yarn breakage based on visual feature enhancement and extraction

作　　者：陈泰芳 周亚勤[1] 汪俊亮 徐楚桥 李冬武 CHEN Taifang;ZHOU Yaqin;WANG Junliang;XU Chuqiao;LI Dongwu(College of Mechanical Engineering,Donghua University,Shanghai 201620,China;Institute of Artificial Intelligence,Donghua University,Shanghai 201620,China;School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200030,China)

机构地区：[1]东华大学机械工程学院,上海201620 [2]东华大学人工智能研究院,上海201620 [3]上海交通大学机械与动力工程学院,上海200030

出　　处：《纺织学报》2023年第8期63-72,共10页Journal of Textile Research

基　　金：上海市教委晨光计划资助项目(20CG41);国家工信部智能制造公共服务平台项目(2021-0173-2-1)。

摘　　要：针对动态生产环境下纱线特征弱、纱线目标小而导致的断头检测正确率低问题,提出了一种基于视觉特征强化提取的细纱断头在线检测方法。为验证算法有效性,搭建了巡游检测装置及图像采集系统,提出了针对纱线弱特征问题的强化算子,实现对拖影纱线特征的强化;设计了针对纱线小目标问题的启发于谷底的小目标分割算法,可自适应地从强化特征后的纱线图像中准确提取纱线特征;最后利用欧拉距离判别法进行纱线断头的检测。通过采集某纺纱厂1000张细纱图片进行实例验证,结果表明本方法检测准确率能够达到97.3%,每张图的处理时间为59.76 ms,能够实时有效地进行断头检测。Objective The yarn breakage in ring spinning directly affects the production efficiency and product quality of the yarns.At present,the commonly used automatic yarn break detection mainly employs a single spindle detection method with photoelectricity or magnetoelectricity as the core,both requiring costly modification of the spinning machine and is difficult to implement.Therefore,this paper proposes a break detection method based on machine vision,which provides a new direction for achieving low-cost and high-precision break detection.Method An online detection method for yarn breaks based on visual feature reinforcement extraction was proposed.For the problem of difficult yarn feature extraction due to yarn trailing,a neighborhood gradient reinforcement operator was designed for yarn clustering to achieve yarn feature reinforcement.To deal with the problem that yarn targets are small and easily disturbed by environment,an Otsu small target segmentation threshold search method inspired by valley bottom was designed to achieve the segmentation of yarn and background,to extract adaptively yarn features from the yarn image after feature reinforcement,and to enable broken end detection by Euler distance discriminant method.Results Inspection devices were installed at a textile factory in Wuxi to collect data,with 1000 captured images selected for analyze.The factory mainly produces pure cotton high count yarns,with 400 spindles per vehicle.To verify the superiority of the weak feature enhancement proposed in this research,the proposed algorithm was compared with Retinex,homomorphic filtering,and histogram averaging for enhancement experiments.The algorithm developed in this research enhanced the yarn features and effectively suppressed the background areas based on the gradient step characteristics of the yarn(Fig.8(e)).Choices of different weights for neighborhood gradient reinforcement operators had different effects on the separation of yarn features and background features.In order to select the optimal weight

关 键 词：环锭纺纱 断头检测 机器视觉 形态学运算 阈值分割 细沙 

分 类 号：TS111.8[轻工技术与工程—纺织材料与纺织品设计]