基于视觉校准的环锭纺细纱条干特征在线提取方法  

Feature extraction method for ring-spun-yarn evenness online detection based on visual calibration

作　　者：陶静 汪俊亮 徐楚桥 张洁[2] TAO Jing;WANG Junliang;XU Chuqiao;ZHANG Jie(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 200240,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200240

出　　处：《纺织学报》2023年第4期70-77,共8页Journal of Textile Research

基　　金：山东省重点研发计划项目(2021CXGC011004);上海市教委晨光计划项目(20CG41);国家工信部项目(2021-0173-2-1);东华大学中央高校学科交叉重点项目(2232021A-08)。

摘　　要：针对纱线高速回转、毛羽条干交织导致的条干轮廓特征难以准确提取的问题,提出了深度学习与形态学运算融合的在线提取方法,设计了图像在线采集系统与校准定焦方法,为轮廓特征提取提供高质量输入,构建了基于整体嵌套边缘检测神经网络和形态学运算的细纱条干轮廓特征提取重构模型,实现毛羽干扰下的条干轮廓在线准确提取。实验结果表明,所提方法的轮廓提取准度指标OIS-F(optimal image scale)、ODS-F(optimal dataset scale)达到了0.91,平均准确率AP达到了0.89,相对于当前方法提高了7%以上。基于提取的轮廓特征计算的条干不匀CV值,与CT3000均匀度检测仪的平均误差小于4%。Objective The appearance quality of yarn is directly related to its mechanical properties and even economic value.However,manual inspection is still the dominant method in most factories,which are lagging and subjective.Based on machine vision and other emerging technology,in the ring spinning yarn production process of online detection of fine yarn evenness,hairiness and other indicators,so as to drive the classification of yarn drop and other emerging industry,has important theoretical significance and engineering value.Method Accurate contour extraction during online visual inspection of ring spun yarn is difficult because of high speed yarn rotation and interweaving of the hairiness.To solve this problem,a method that fused deep-learning with morphological operations is proposed.Firstly,an online image acquisition system and focusing method are designed to provide high quality input for contour feature extraction;Secondly,a model based on holistically-nested edge detection(HED)neural network and morphological operations is constructed to achieve accurate online contour extraction under the interference of hairiness.Results The camera was deployed to acquire 1600 images of the yarn,whose resolution is 2448 pixel×2048 pixel,to calculate the optimal focal plane with the acquisition parameters.Compared with the images acquired under other focal plane parameters,the images acquired under the calculated focal plane parameters are of higher quality,which obviously improves the accuracy of contour extraction.500 images of yarn were collected using the calibrated image acquisition system and processed with the proposed contour extraction method and other SOTA(state of the art)methods.The proposed method achieves OIS-F(optimal image scale),ODS-F(Optimal Dataset scale)of 0.91 and AP(average precision)of 0.89,which is more than 7%better than the current method.From the visual comparison results,it can be seen that the proposed method is based on the output of HED network,combined with morphological operations to remove

关 键 词：细纱条干均匀度 在线检测 机器视觉 轮廓提取 条干CV值 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TS111.9[自动化与计算机技术—计算机科学与技术]