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通过改进的CLR-YOLOv11算法检测航空发动机除缺陷.

Yi Liu1, Jiatian Liu2, Yaxi Xu3

  • 1Key Laboratory for Civil Aviation Data Governance and Decision Optimization, Civil Aviation Management Institute of China, Beijing 100102, China.

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概括
此摘要是机器生成的。

这项研究介绍了CLR-YOLOv11,这是一个改进的对象检测模型,用于飞机发动机的除. 它通过专门的数据预处理和优化的网络架构来提高检测准确性和效率,从而增强飞机健康管理.

关键词:
这就是YOLOv11的意义.飞机发动机 - 飞机发动机背景指导机制是指导机制.大内核的卷积注意力旋转检测检测器检测器旋转检测器

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科学领域:

  • 航空航天工程 航空航天工程
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 航空发动机除检测对于飞机健康管理至关重要.
  • 现有的方法在高计算成本和有限的局部特征提取方面扎.
  • 基于旋转的对象检测在这个领域面临效率和准确性挑战.

研究的目的:

  • 开发了一种改进的YOLOv11算法,以高效准确地检测航空发动机的废弃物.
  • 解决当前模型中计算复杂性和局部特征提取的局限性.
  • 通过优化对象检测来增强实时航空检查能力.

主要方法:

  • 拟议的CLR-YOLOv11模型整合了上下文引导的大内核注意力和旋转检测头.
  • 实施了具有几何和混合增强的有针对性的数据预处理管道,随后进行Z-Score规范化.
  • 引入了上下文导向的特征融合 (C3K2CG) 和效率导向的大核心注意 (C2PSLA) 模块.

主要成果:

  • 在自制的航空发动机废弃数据集上实现了78.5%的mAP@0.5:0.95.
  • 与基线YOLOv11-obb模型相比,在没有专门的数据增强的情况下表现出4.2%的改善.
  • CLR-YOLOv11模型在检测准确性和计算效率上都显示出显著的收益.

结论:

  • CLR-YOLOv11型号为高精度,实时的航空发动机除检测提供了有效的解决方案.
  • 协同结构优化,包括数据预处理和注意力机制,提高了检测性能.
  • 这项研究通过改进的视觉检查技术,有助于推进飞机健康管理.