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织物结构参数的高效识别是纺织品智能制造中的关键环节，随着计算机技术的发展，传统算法和深度学习算法已广泛应用于该领域。然而，当前该方向的系统性研究仍较相对缺乏。为此，系统综述织物结构参数自动识别技术的研究现状，深入剖析传统算法与深度学习算法在纱线密度、织物组织及组织循环识别等关键参数识别中的应用与最新进展。探讨当前技术面临的挑战，并对未来研究方向进行展望。相关研究推动了织物结构参数识别技术的进一步发展，为纺织智能制造提供了理论依据与技术支撑。
Abstract：Efficient identification of fabric structural parameters is a key step in the intelligent manufacturing of textiles. With the advancement of computer technology, both traditional algorithms and deep learning algorithms have been widely applied in this field. However, systematic research in this area remains relatively limited. The current status of automatic fabric structure parameter identification technology was systematically examined, and an in-depth analysis was conducted on the application and recent progress of traditional algorithms and deep learning algorithms in identifying key parameters such as yarn density, fabric weave, and pattern repeat units. The challenges faced by current technologies were further discussed, and an outlook on future research directions was provided. These efforts contribute to the further development of fabric structure parameter recognition technology and provide a theoretical foundation and technical support for the advancement of intelligent textile manufacturing.
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基本信息:
DOI：10.19507/j.cnki.1673-0356.2026.03.017
中图分类号:TS107;TP391.41;TP18
引用信息:
[1]李新格,李娜娜,张效栋.基于计算机视觉的织物结构参数识别技术综述[J].纺织科技进展,2026,48(03):1-6.DOI:10.19507/j.cnki.1673-0356.2026.03.017.
2025-04-16
2025
2025-05-27
2025
1
2026-03-25
2026-03-25