The hairiness index is an important aspect of evaluating the quality of the yarn, and it is also an important basis for reflecting the quality of the textile process and yarn processing components. The length, number and distribution of hairiness not only affect the efficiency and quality of weaving and knitting, but also directly affect the appearance and price of the final product. The evaluation index includes the number, length and area of ​​hairiness.
Manual detection is affected by the working environment and labor intensity, and the detection efficiency is low. Xian Kede uses machine vision image processing technology to deal with hairiness defects in the process of cop yarn production, and proposes a yarn hairiness detection method based on improved median filter and maximum variance between classes to effectively avoid the above-mentioned defects and further improve Hairiness detection level.
Build a digital system of casing yarn hairiness for image acquisition and processing. Firstly, the yarn image is preprocessed by using grayscale transformation and grayscale stretching. Then, the median filter is used to perform template filtering on the image of the bobbin. Finally, a clear hairiness binary image is obtained by using the maximum cluster-like variance method. Hairiness number, length and assessment of product qualification.
At present, digital image processing technology is widely applied to the detection of yarn hairiness. From the research results, the yarn hairiness detection effect is good, but there are two problems in the detection of yarn hairiness: First, the surface of the yarn is susceptible to the environment, showing uneven light and dark, affecting hairiness feature extraction; It is the structure of the upper and lower parts of the cop yarn that require different pretreatment of the two parts. In view of the above reasons, the automatic appearance detection system of Xie Kede Guanshee adopts an improved median filter for hairiness images, which overcomes the problems that the traditional filtering methods can easily cause image distortion and detail loss; the maximum between-class variance method can accurately segment the cop yarns. Hairy feathers.
HDGS-I yarn hairiness acquisition system mainly includes three parts: industrial camera, Led visual light source, and computer.
In order to ensure that the captured yarn image remains clear, the camera parameters are set as follows: exposure time 800 μs, image resolution 800 × 1000, to ensure that the camera's center point and the center of the bobbin yarn in the same position, reduce the camera shooting Image distortion. The distance between the cop and the lens surface is maintained at about 20 cm. A clear hairiness image is obtained by adjusting the brightness, focal length and aperture size of the light source. After the hairiness image is subjected to grayscale transformation, grayscale stretching and improved median filtering, the resulting hairiness binary image is complete and clear, which can better reflect the physical information of hairiness.
Xi'an has built a digital image acquisition system for the yarn hairiness of the yarns for sampling, and uses grayscale transformation and grayscale stretching to preprocess images, effectively enhancing the differences between the target hairiness and the background. Then use the improved median filter to remove the interference in the hairiness image and obtain a clear hairiness binary image. Finally, a clear hairiness image is obtained by using the maximum cluster-like variance method. Based on this, hairiness information is obtained. Experiments show that the system can accurately measure the number and length of hairs.
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