关键词: 机器视觉, 落纱机, 纱锭识别, 管纱识别

Abstract: In the textile manufacturing industry, the process of yarn dropping in spinning machine is critical as it directly impacts yarn quality, production output, and factory operations. Traditional automatic fine yarn dropping machines, while capable of performing operations automatically, often suffer from imprecise of operations, resulting in missed of empty tube insertion or tube yarn extraction during the yarn dropping process. The current coping approach is to assign dedicated personnel to track, inspect, and address the issue, which increases labor costs and restricts the level of automation and intelligence in the workshop.
This paper aims to address the issues of yarn tube extraction missing and empty tube insertion missing in traditional automatic fine yarn dropping machines by introducing machine vision technology to accurately identify tube yarns and yarn spindles, thereby reducing the probability of missed extractions and insertions in subsequent operations. However, traditional image processing techniques face challenges in complex fine yarn workshop environments, such as complex target backgrounds, lighting variations, obstructions, and shooting angles. In addition, it is necessary to set parameters and thresholds based on experience during the identification process of yarn spindles and tube yarns, which increases the difficulty of recognition. Therefore, accurate identification of tube yarns and yarn spindles is crucial for automatic yarn dropping machines based on machine vision technology. To solve the interference problem of images gathered by visual sensors in workshop environments, this paper proposes a basic image processing method. By adjusting the contrast between the objects and the background, the characteristics of yarn spindles and tube yarns can be highlighted. This method also performs initial and secondary identification in the objects identification process to  obtain the object areas more accurately. In order to further improve the identification accuracy of yarn spindles and tube yarns, the basic image processing method is improved in image preprocessing phase and object identification phase respectively. In the preprocessing phase, image multiplication fusion is used to obtain high-contrast images to reduce the difficulty of subsequent image segmentation. For the interference introduced by the support rod of the spinning machine in the original image, find the nearest two horizontal contour bodies in the object region, and eliminate the smaller contour body in the object selection decision, until the remaining identification quantity is consistent with the set number.
The experimental results demonstrate that the identification accuracy of yarn spindles and tube yarns in different scenarios exceeded 98.40%, realizing the accurate identification of spindles and tube yarn in complex background. In future work, machine learning methods can be introduced to solve the problem that individual parameter setting cannot adapt to special scenes such as strong light and reflection, and further improve the recognition accuracy of spindles and pipe yarn.

Key words: machine vision, doffer, spindle identification, yarn identification