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织行业实时布料缺陷检查的YOLO对象检测:对YOLOv1到YOLOv11的审查
1Department of Software Convergence, Soonchunhyang University, Asan-si 31538, Republic of Korea.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
本综述探讨了用于自动化织物缺陷检测的You Only Look Once (YOLO) 对象检测框架的演变. 它比较了YOLO版本,讨论了挑战,并为织品质量控制提出了AI驱动的解决方案.
科学领域:
- 计算机视觉 计算机视觉
- 织制造业 织制造业 织制造业 织制造业 织制造业
- 人工智能的人工智能
背景情况:
- 自动布料缺陷检测对于织品质量控制至关重要,提高效率并减少制造过程中的人为错误.
- 传统的检查方法是主观和不一致的,特别是在高速生产中.
研究的目的:
- 系统地审查从YOLO-v1到YOLO-v11的你只看一次 (YOLO) 对象检测框架的演变.
- 分析建筑进步及其对织物缺陷检测的影响.
- 为提供用于织物检查应用的YOLO系列的全面比较.
主要方法:
- 对YOLO物体检测框架演变的系统审查 (v1-v11).
- 对建筑进步的分析,包括注意力机制和变压器集成.
- 对用于织物缺陷检测的YOLO变体进行比较研究.
主要成果:
- YOLO框架已经显著发展,进步提高了织物缺陷检测能力.
- 关键的创新包括基于注意力的功能改进和变压器集成.
- 综合性比较强调了不同YOLO版本的实际含义.
结论:
- YOLO框架为织行业的智能织物缺陷检测提供了一个强大的工具.
- 应对数据集限制和计算约束等挑战对于未来的发展至关重要.
- 未来的解决方案包括合成数据,联合学习和边缘人工智能,以优化质量控制.
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