SFCF-Net:空间频率协同学习用于工业放射检查中的服务前飞机发动机叶片的造缺陷细分
Shun Wang1, Zhiying Sun1, Xifeng Fang1
1School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
一个新的网络,SFCF-Net,通过准确检测轮机叶片造缺陷来提高飞机发动机安全. 它结合了空间和频域特征,克服了现有方法的局限性,以便更好地对缺陷进行细分.
科学领域:
- 材料科学与工程 材料科学与工程
- 计算机视觉和人工智能的人工智能
- 航空航天工程 航空航天工程
背景情况:
- 轮机叶片是飞机发动机的重要部件,但制造缺陷会带来安全风险.
- 目前的深度学习缺陷检测方法与图像质量差,各种缺陷外观和复杂的几何形状作斗争.
- 依赖空间域特征限制了微妙的检测,基于纹理的缺陷.
研究的目的:
- 开发一个先进的深度学习模型,用于准确的轮叶片缺陷检测和细分.
- 解决现有方法在处理图像质量差和复杂缺陷模式方面的局限性.
- 通过改进自动化质量控制来提高飞机发动机的安全性.
主要方法:
- 提出了空间和频率互补融合网络 (SFCF-Net),将空间和频率域特征集成.
- 引入了选择性交叉模式校准 (SCC) 模块,用于在图像条件差的情况下保存细粒度的细节.
- 开发了一种交叉模式的改进和补充 (CRC) 模块,具有针对强大的缺陷歧视的注意力机制,以及用于几何特征的新型不对称窗口注意力 (AWA) 模块.
主要成果:
- 与ATBCD-Seg数据集和公共基准的最先进方法相比,SFCF-Net在缺陷细分方面表现优越.
- 拟议的模块有效地处理了图像质量差,类内差异,类间相似性和不规则的缺陷形状.
- 在保持类内一致性的情况下,在区分类似的缺陷类别中实现了高精度.
结论:
- SFCF-Net为轮机叶片造缺陷细分提供了强大而准确的解决方案.
- 空间和频域特征的协同融合对于复杂的缺陷检测是有效的.
- 该方法符合飞机发动机制造中自动化质量控制的实际要求.
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