Detecting various parameters of woven fabrics is one of the important methods to evaluate the quality of fabrics.In the early stage of industrial development,fabrics were mainly relied on manual to determine the quali...Detecting various parameters of woven fabrics is one of the important methods to evaluate the quality of fabrics.In the early stage of industrial development,fabrics were mainly relied on manual to determine the quality,which was inefficient and unstable,so intelligent inspection is a popular development trend today.In recent years,computer vision technology has been widely used in the fields of fabric density measurement,color analysis,and weave pattern recognition.Based on the above three aspects,the advanced research progress of global researchers is reviewed in this paper and the shortcomings of current research and possible research directions in the future are analyzed.Computer vision technology is not only objective evaluation,but also has the advantages of accuracy and efficiency,and has a good development prospect in the field of textiles.展开更多
Currently, numerous automatic fabric defect detection algorithms have been proposed. Traditional machine vision algorithms that set separate parameters for different textures and defects rely on the manual design of c...Currently, numerous automatic fabric defect detection algorithms have been proposed. Traditional machine vision algorithms that set separate parameters for different textures and defects rely on the manual design of corresponding features to complete the detection. To overcome the limitations of traditional algorithms, deep learning-based correlative algorithms can extract more complex image features and perform better in image classification and object detection. A pixel-level defect segmentation methodology using DeepLabv3+, a classical semantic segmentation network, is proposed in this paper. Based on ResNet-18,ResNet-50 and Mobilenetv2, three DeepLabv3+ networks are constructed, which are trained and tested from data sets produced by capturing or publicizing images. The experimental results show that the performance of three DeepLabv3+ networks is close to one another on the four indicators proposed(Precision, Recall, F1-score and Accuracy), proving them to achieve defect detection and semantic segmentation, which provide new ideas and technical support for fabric defect detection.展开更多
基金National Natural Science Foundation of China(No.61876106)Shanghai Natural Science Foundation of China(No.18ZR1416600)+1 种基金Shanghai Local Capacity-Building Project,China(No.19030501200)Zhihong Scholars Plan of Shanghai University of Engineering Science,China(No.2018RC032017)。
文摘Detecting various parameters of woven fabrics is one of the important methods to evaluate the quality of fabrics.In the early stage of industrial development,fabrics were mainly relied on manual to determine the quality,which was inefficient and unstable,so intelligent inspection is a popular development trend today.In recent years,computer vision technology has been widely used in the fields of fabric density measurement,color analysis,and weave pattern recognition.Based on the above three aspects,the advanced research progress of global researchers is reviewed in this paper and the shortcomings of current research and possible research directions in the future are analyzed.Computer vision technology is not only objective evaluation,but also has the advantages of accuracy and efficiency,and has a good development prospect in the field of textiles.
基金Supported by the National Natural Science Foundation of China(61876106)Shanghai Local Capacity-Building Project(19030501200)。
文摘Currently, numerous automatic fabric defect detection algorithms have been proposed. Traditional machine vision algorithms that set separate parameters for different textures and defects rely on the manual design of corresponding features to complete the detection. To overcome the limitations of traditional algorithms, deep learning-based correlative algorithms can extract more complex image features and perform better in image classification and object detection. A pixel-level defect segmentation methodology using DeepLabv3+, a classical semantic segmentation network, is proposed in this paper. Based on ResNet-18,ResNet-50 and Mobilenetv2, three DeepLabv3+ networks are constructed, which are trained and tested from data sets produced by capturing or publicizing images. The experimental results show that the performance of three DeepLabv3+ networks is close to one another on the four indicators proposed(Precision, Recall, F1-score and Accuracy), proving them to achieve defect detection and semantic segmentation, which provide new ideas and technical support for fabric defect detection.
