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• W3097776100 abstract "Cotton foreign fibers directly affect the quality of a textile product; the less foreign fibers in raw cotton, the higher the quality grade of the textile product. Based on the foreign fiber clean machine, this paper proposed an evaluation method of foreign fiber content using deep learning. First of all, a large number of images of foreign fibers were collected from different production lines and annotated to obtain the mask image dataset of foreign fibers. Secondly, by comparing the image segmentation algorithm based on deep learning, tests showed that U-Net has a better performance on different segment metrics evaluations, and U-Net is improved to realize the real-time segmentation of foreign fiber images. The actual size of the foreign fiber could be calculated through the combination of the segment result and the mechanical parameters of the machine. Finally, the test results showed that the relative error between the estimated size and the actual size was less than 4%. After the prototype test, the algorithm was deployed on the actual production line and, by comparing the algorithm data in a random time with the actual foreign fiber statistical data, the overall error was less than 2%. The test showed that the new evaluation method can fully reflect the content of foreign fiber in raw cotton." @default.
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• W3097776100 date "2020-10-29" @default.
• W3097776100 modified "2023-09-23" @default.
• W3097776100 title "Content Estimation of Foreign Fibers in Cotton Based on Deep Learning" @default.
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• W3097776100 doi "https://doi.org/10.3390/electronics9111795" @default.
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