Tianbao Zhai

For improving the efficiency, flexibility and robustness of yarn defect system, a new method is proposed to evaluate the quality of yarn by testing the yarn diameter, defects and hairiness based on machine vision and image processing technology. Firstly, the diameter image processing unit (DIPU) is defined and a series of sampling points are selected from moving yarn, and the DIPU corresponding to each sampling point is segmented from the captured yarn images. The average diameter of DIPU is used to represent the yarn diameter of the test points. In the extraction of yarn images, the DIPU is divided into definite foreground region, definite background region and unknown region according to the characteristics of gray-level projection distribution, and the unknown region is further processed with Poisson matting method, in which the yarn image and background image are completely separated by a defined connectivity classifier. After the yarn core is extracted by the classifier, the hairiness is divided by using image subtraction. Finally, in order to further evaluate the quality of yarn, the yarn defects were analyzed by the method of statistical methods.

In order to overcome the shortcomings of time-consuming and inefficient manual detection methods of fabric density, a fast and efficient automatic detection method of fabric density based on the digital grating is proposed. In order to realize automatic fabric density detection, the design method of the digital grating with gradual constant is described in detail on the basis of the principle of moiri pattern. The moiri pattern containing yarn density information of the fabric can be obtained by superimposing the captured fabric image and the digital grating image. After the moiri pattern is projected, the fabric density can be accurately determined by analyzing the distribution characteristics of projection curve. In the detection process, a digital grating image generated by computer is used to replace the physical grating, which not only makes the detection faster, but also make the system applicable for the fabric density detection of all kinds of specifications. Testing experiments were carried out on various fabric samples including fabrics in a plain weave and in a twill weave, the detection accuracy is more than 99.2 and the detection speed is much faster than the existing methods, which can meet the requirements of on-line detection in fabric production.