一个联合设计的框架,结合了圆顶孔径成像和生成人工智能,用于在非平面金属表面检测缺陷
Zhongqing Jia1, Zhaohui Yu1, Chen Guan1
1Shandong Key Laboratory of Optoelectronic Sensing Technologies, National-Local Joint Engineering Laboratory for Energy and Environment Fiber Smart Sensing Technologies, Laser Institute, Qilu University of Technology (Shandong Academy of Sciences), 3501 Daxue Road, Changqing District, Jinan 250353, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
本研究引入了一种用于检查金属零件的新系统,使用先进的人工智能创建真实的缺陷图像. 这提高了自动缺陷检测,使制造更安全,更高效.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 制造业 制造技术 制造技术
背景情况:
- 由于复杂的形状,反射表面和很少的缺陷例子,对安全关键的金属组件进行自动视觉检查是困难的.
- 传统的2D方法面临着成像问题和有限数据的不良性能,而3D方法通常是缓慢和昂贵的.
研究的目的:
- 开发一个完整的系统,用于缺陷成像,生成和检测具有挑战性的金属元件.
- 通过共同设计的成像和深度生成模型来增强缺陷感知能力.
主要方法:
- 开发了一种专门的成像系统,采用圆顶照明和用于非平面金属表面的小孔径镜头.
- 实施了双阶段生成策略,使用改进的FastGAN (DMGF-SLE) 进行缺陷补丁和Poisson图像编辑以实现无融合.
- 利用感知和优化损失功能,专注于现实的缺陷生成.
主要成果:
- 在几次射击条件下高效生成现实的缺陷样本,比基线模型提高了11-24%的Fréchet Inception Distance (FID) 得分.
- 显著提高了下游检测性能,使用合成数据将YOLOv8的mAP@50:95从50.4%提高到60.5%.
- 展示了一个计算效率高且实际可行的系统解决方案.
结论:
- 拟议的综合系统提供了一个完整的,协同的,可部署的解决方案,用于检测具有挑战性的金属元件的缺陷.
- 这种方法为改善制造业自动化视觉检查提供了可行的技术途径.
- 图像和生成模型的共同设计有效地解决了当前缺陷检测方法的局限性.
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