:,

Yuan Gao1, Yu Wang1, Xiaoguang Tu1,2

  • 1College of Aviation Electronic and Electrical Engineering, Civil Aviation Flight University of China, Chengdu 641450, China.

Journal of imaging
|September 26, 2025
PubMed
概括

通过使用特征适配器和双阶段的中心神经学习来弥合域间隙,AnomNet增强了工业异常检测. 这种强大的框架在识别缺陷方面实现了高准确性,以提高产品质量和安全性.

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