基于机器视觉的缝纫线迹缺陷检测方法  

Machine vision-based defect detection method for sewing stitch traces

作　　者：陈育帆 郑小虎 徐修亮 刘冰 CHEN Yufan;ZHENG Xiaohu;XU Xiuliang;LIU Bing(College of Information Science and Technology,Donghua University,Shanghai 201620,China;Institute of Artificial Intelligence,Donghua University,Shanghai 201620,China;Engineering Research Center of Artificial Intelligence for Textile Industry,Ministry of Education,Shanghai 201620,China;Shanghai Industrial Big Data and Intelligent Systems Engineering Technology Center,Shanghai 201620,China;HIKARI(Shanghai)Precise Machinery Scientific&Technology Co.,Ltd.,Shanghai 201599,China;Hangzhou Zhongfu Technology&Innovation Research Institute Co.,Ltd.,Hangzhou,Zhejiang 311199,China)

机构地区：[1]东华大学信息科学与技术学院,上海201620 [2]东华大学人工智能研究院,上海201620 [3]纺织工业人工智能技术教育部工程研究中心,上海201620 [4]上海工业大数据与智能系统工程技术研究中心,上海201620 [5]上海富山精密机械科技有限公司,上海201599 [6]杭州中服科创研究院有限公司,浙江杭州311199

出　　处：《纺织学报》2024年第7期173-180,共8页Journal of Textile Research

基　　金：中央高校基本科研业务费专项资金资助项目(2232021D-15);上海市科技计划项目(20DZ2251400)。

摘　　要：缝纫线迹缺陷检测过程易受缝纫机抖动和面料移动过快等影响,针对缺陷检测过程中的扰动影响,以高精度和快速检测缺陷特征为目标,提出一种基于机器视觉的缝纫线迹缺陷检测方法。首先将主干网络的标准卷积改用蓝图卷积的DeblurGAN-v2算法和拉普拉斯算法联用,分辨模糊与清晰图像,并对运动模糊图像去模糊。然后将师生特征金字塔匹配算法应用到缝纫线迹缺陷检测上,将困难样本挖掘技术应用到师生特征金字塔匹配算法中提高了算法的检测精度与速度。结果表明:图像去模糊算法有效地去除了由外部干扰引起的图像模糊问题,缺陷检测算法检测正确率保持在95%以上,单张图片检测速度在0.04 s以下。本文方法能有效检测线迹缺陷特征,保障生产的高效性和连续性。Objective In order to solve the problems of slow speed,low efficiency,and high cost in conventional manual quality inspection methods for sewing thread,this study proposes a machine vision-based method for sewing thread defect detection in seams.This study aims to achieve fast,accurate,and automated identification of common defects such as cast thread,jumper thread,and broken thread in seams.This study also highlights the importance and necessity of improving product quality and production efficiency in the textile and garment industry.Method This study adopts a two-step approach for defect detection.Firstly,a low-cost array camera was adopted to capture real-time images of the sewing seam and the DeblurGAN-v2 method was employed to remove motion blurriness from the images,aiming at improving image clarity.Secondly,the student-teacher feature pyramid matching method was applied for anomaly detection,which transfers the knowledge from a pre-trained ResNet-34 model as the teacher network to a student network with the same architecture,so as to learn the distribution of normal images.By comparing the differences between the feature pyramids generated by the two networks as a scoring method,the defect detection system made decisions on whether the image has anomalies,and marked the abnormal areas with a heat distribution map.Results The defects of flat stitch fabric and overstitch fabric were tested and the performance of the proposed method was evaluated in terms of recall and accuracy rates.The results show that the proposed method can effectively detect various sewing thread defects and has high recall and accuracy rates for different types of defects.This study also provided some examples of defect detection results and scores for different types of defects.Conclusion The feature pyramid matching technique is applied in the field of stitch trace detection.By adding the difficult sample mining technology,the average detection accuracy is increased to more than 95%,and the detection speed of a single image is less

关 键 词：机器视觉 缝纫线迹 缺陷检测 DeblurGAN-v2算法 服装质量 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TS103[自动化与计算机技术—控制科学与工程]