Automatic inspection of yarn-dyed fabric density by mathematical statistics of sub-images

Abstract

To inspect the yarn-dyed fabric density automatically, an effective image analysis method based on mathematical statistics of sub-images is proposed in this paper. This method consists of two main steps: rough measurement and precise measurement. The rough measurement is based on projection curve of the whole fabric image. The fabric image is converted into HSV model from RGB model firstly, and then the projection curve of value is gained directly. The number of yarns is obtained by counting the number of peaks in the curve roughly. The precise measurement is based on projection curves of the fabric sub-images. According to the roughly estimated yarn number, the whole fabric image is divided into a certain amount of sub-images and the projection method is applied to all the sub-images, respectively. The probability distribution map of peaks is obtained by processing the projection curves of all sub-images and the positions of the yarn center are located in the frequency curve generated from the map by mathematical statistics method. The number of peaks in the frequency curve is counted, and, therefore, the number of yarns is detected, and the density can be calculated precisely. The experimental results proved that the proposed method is effective for yarn-dyed fabrics and can satisfy the requirement for production practice.

Keywords:

• yarn-dyed fabric
• fabric density
• projection curve
• mathematical statistics
• data smoothing
• frequency curve

Additional information

Funding

Funding. The authors would like to acknowledge the National Natural Science Foundation of China [No. 61202310]; the Natural Science Foundation of Jiangsu Province [BK2011156]; the Open Project Program of Key Laboratory of Eco-Textiles (Jiangnan University), Ministry of Education, China [No. KLET 1110]; Research Fund for the Doctoral Program of Higher Education of China [20120093130001]; the Henry Fok Educational Foundation [141071]; the National Postdoctoral Fund Project [2013M541602]; the Postdoctoral Fund Project of Jiangsu Province [1301075C].