基于机器视觉和注意力机制的织回收废弃服装的定性分类
Rui Tian1, Ziqi Lv1, Yuhan Fan2
1School of Chemical and Environmental Engineering, China University of Mining and Technology-Beijing, Beijing 100083, PR China; Inner Mongolia Research Institute, China University of Mining and Technology-Beijing, Ordos 017001, PR China.
通过智能机器视觉系统自动化服装分类,大大提高了回收效率. 这种先进的系统实现了人类水平的准确性,解决了诸如色彩相似性和织废料变形等挑战.
科学领域:
- 织品回收和废物管理
- 计算机视觉和机器学习
- 工业自动化工业自动化
背景情况:
- 越来越多的织废物需要有效的回收解决方案.
- 手工服装分类是劳动密集型,低效和危险的.
- 现有的自动系统在与视觉复杂性,如变形和遮等问题中扎.
研究的目的:
- 开发一种智能机器视觉系统,用于自动化服装分类.
- 提高织废物处理的分类准确性和效率.
- 为了解决当前自动服装分类方法的局限性.
主要方法:
- 使用自己开发的平台,策划了约27,000件废弃服装的数据集.
- 在机器视觉系统中实现了注意力机制.
- 与最先进的方法进行了基准测试,并进行了为期两周的在线部署.
主要成果:
- 在各种模型中显著提高了分类准确性.
- 从最初的68.28%达到人类水平 (>90%) 的性能.
- 证明有效处理复杂的视觉挑战和增强系统稳定性.
结论:
- 增强的智能机器视觉系统有效地自动化了服装分类.
- 该系统在提高服装回收的效率和安全性方面表现有前途.
- 注意力机制对于处理服装废物分类中的视觉复杂性至关重要.
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