基于多度量多模型图像投票的织物表面瑕疵检测方法  

Detection of fabric surface defects based on multi-metric-multi-model image voting

作　　者：朱凌云[1,2] 王晨宇 赵悦莹 ZHU Lingyun;WANG Chenyu;ZHAO Yueying(College of Computer Science and Engineering,Chongqing University of Technology,Chongqing 400054,China;Liangjiang International College,Chongqing University of Technology,Chongqing 401135,China)

机构地区：[1]重庆理工大学计算机科学与工程学院,重庆400054 [2]重庆理工大学两江国际学院,重庆401135

出　　处：《纺织学报》2024年第6期89-97,共9页Journal of Textile Research

基　　金：重庆市巴南区科技计划项目(2018TJ02,2020QC430);重庆理工大学研究生教育高质量发展项目(gzlcx20223133)。

摘　　要：为解决自动化生产线上织物表面瑕疵检测准确率低和计算速度慢的问题,利用织物表面具有周期纹理的特性提出了一种改进的RANSac检测方法,即多度量多模型图像投票。首先将输入图像裁剪为尺寸一致的子图,计算出子图多维度量的输出值矩阵;然后与改进RANSac计算出的无瑕疵背景的多维度量标准值分别对应作差,采用投票得出每张子图的基础分;再将其在4个记数模型下得到的综合评分排序,根据顺序和偏移量在输出端得到外点所代表的瑕疵子图。实验结果表明:在自采样的织物瑕疵数据集上,选择单度量和单模型的预测精度平均可达到90.9%,平均预测时间达到0.139 s,综合多度量多模型投票的平均预测精度可达到92.7%。该算法不需要大量前期数据进行训练,适用于纯色和条纹状织物的实时表面缺陷检测。Objective Fabric surface defects influence the textile output,quality,price,and other factors directly,and it is hence necessary to devise a method for detecting fabric surface defects quickly and accurately in automatic production lines.This research aims to establish a statistical algorithm to achieve rapid detection of fabric surface defects.Method Partial defects on the fabric surface could destroy its periodic geometric and statistical characteristics.Based on this feature,a detection method combining with an improved RANSac,named multi-metric-multi-model image voting(MMIV),was proposed.The input image was firstly divided into sub-images of the same size,and the output value matrix of the sub-image multi-dimensional metric was calculated.They were different from the multi-dimensional measurement standard values of the flawless background calculated by the improved Zero-Slope-RANSac method,and the basic scores of each sub-image were obtained by voting.Then the comprehensive scores obtained under the 4 counting models(square of standard mean,Borda,Copeland,Maximin)were sorted,finally,the defect sub-image represented by the outer point was obtained at the output end according to the sequence and offset.Result The tested subjects were the self-sampled fabric defect dataset.When the RANSac method parameter was set to 3 and threshold set to 2,the confidence was greater than 0.25 and the prediction accuracy of single-measure-single-model reached 89.3%on average.The prediction accuracy reached 95.6%when the gray mean measure and Borda ranking model were selected,which was the highest,while the square of standard mean model(SSM model)had the lowest accuracy.Accuracy under 4-measures-3-models showed the prediction of 2565 grey fabric images with non-latticed texture,and that of 3708 grey fabric images with latticed texture background.The confidences of the both tables were greater than 0.35,and the prediction accuracy of each model was compared with the values of RANSac parameter set from 1 to 3,and the threshold set

关 键 词：目标检测 周期纹理 织物表面瑕疵检测 零斜率RANSac 多度量多模型图像投票 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TS101.8[自动化与计算机技术—计算机科学与技术]