基于机器视觉的复杂环境下细纱落纱机的纱锭和管纱识别OA北大核心CSTPCD

In the textile manufacturing industry,the process of yarn dropping in spinning frames is critical as it directly impacts yarn quality,production output,and factory operations.Traditional automatic doffers,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 doffing process.The current 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 aimed to address the issues of yarn tube extraction missing and empty tube insertion missing in traditional automatic fine yarn doffers by introducing machine vision technology to accurately recognize tube yarns and yarn spindles,so as to reduce 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 recognition process of spindles and tube yarns,which increases the difficulty of recognition.Therefore,accurate recognition of tube yarns and spindles is crucial for automatic doffers based on machine vision technology.To solve the interference problem of images gathered by visual sensors in workshop environments,this paper proposed a basic image processing method.By adjusting the contrast between the targets and the background,the characteristics of spindles and tube yarns could be highlighted.This method also performed initial and secondary recognition in the targets recognition process to obtain the target areas more accurately.In order to further improve the recognition accuracy of yarn spindles and tube yarns,the basic image processing method was improved in image preprocessing phase and object recognition phase,respectively.In the preprocessing phase,image multiplication fusion was 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,the study found the nearest two horizontal contour bodies in the target region,and eliminated the smaller contour body in the target selection decision,until the remaining recognition quantity was consistent with the set number. The experimental results demonstrate that the recognition accuracy of yarn spindles and tube yarns in different scenarios exceeds 98.40%,realizing the accurate recognition of spindles and tube yarns 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 yarns.