A computer vision-based system for automatic detection of misarranged warp yarns in yarn-dyed fabric. Part I: continuous segmentation of warp yarns

Abstract

In textile and garment industries, misarranged warp yarns of yarn-dyed fabrics disorganize the layout of fabrics and lead to poor product quality. This series of studies aims to develop a computer vision-based system for automatic detection of misarranged color warp yarns in terms of high efficiency and good accuracy. Four main parts are included in this series of studies: warp yarn segmentation, fabric image stitching, warp regional segmentation, and yarn layout proofing. This paper proposes a continuous segmentation method of warp yarns to detect the misarranged color warp yarns for yarn-dyed fabrics automatically, which is the foundation of the developed computer vision-based system. The proposed framework consists of two main components: warp yarn segmentation and fabric image stitching. Firstly, the sequence images of a fabric stripe are captured using a designed offline image acquisition platform. Secondly, the warp yarns in the sequence images are segmented by a sub-image projection-based method successively. Thirdly, the sequence images are stitched by a yarn-template matching method based on their warp segmentation results. Finally, the continuous segmentation result of warp yarns is saved for the further processing of warp regional segmentation and color warp layout proofing. The proposed method has been evaluated on 720 fabric images of five fabric examples with plain and 2/2 twill, and experimental results show that the proposed method can realize the continuous segmentation of warp yarns in yarn-dyed fabrics with the yarn segmentation accuracy of 97.43% and image stitching accuracy of 99.53%.

Keywords:

• Misarranged color yarns
• layout of color yarns
• yarn-dyed fabric
• yarn segmentation
• fabric image stitching

Acknowledgments

The authors would like to acknowledge the National Natural Science Foundation of China [grant number 61202310]; Research Fund for the Doctoral Program of Higher Education of China [grant number 20120093130001]; the Henry Fok Educational Foundation [grant number 141071]; the National Postdoctoral Fund Project [grant number 2013M541602]; the Postdoctoral Fund Project of Jiangsu Province [grant number 1301075C]; Prospective Industry University Research Project of Jiangsu Province [grant number BY2013015-20]; the Postgraduate Research Project of Jiangsu Province [grant number KYLX15_1180].