基于LFD-YOLO框架的航空发动机轮机叶片的新型表面缺陷检测方法研究
Wei Deng1, Guixiong Liu1, Jun Meng2
1School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
本研究介绍了一种先进的方法,用于检测航空发动机轮机叶片 (ATB) 的表面缺陷,提高准确性和效率. 这种新的方法确保精确识别即使是微小的缺陷,满足关键的行业需求.
科学领域:
- 材料科学 材料科学 材料科学
- 航空航天工程 航空航天工程
- 计算机视觉 计算机视觉
背景情况:
- 在航空发动机轮机叶片 (ATB) 上的表面缺陷检测面临着精度和效率低的挑战.
- 现有的方法很难识别微小的缺陷并满足实时性能要求.
研究的目的:
- 开发一种新的,高度准确和高效的ATB表面缺陷检测方法.
- 为解决关键航空航天部件当前缺陷检测技术的局限性.
主要方法:
- 使用LDconv模型进行动态卷积内核调整.
- 集成可变形注意力机制 (DAT) 以有效捕捉微小的缺陷特征.
- 使用Focaler-CIoU优化界限框丢失函数.
主要成果:
- 实现了96.2%的平均平均精度 (mAP_0.5).
- 达到了96.7%的F测量和98.8%的识别率 (I).
- 保持了超过每秒25张图像的检测速度.
结论:
- 拟议的方法显著提高了ATB表面缺陷检测的精度和效率.
- 该方法符合航空航天应用中对精度和实时性能的严格要求.
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