Pipeline working mode based on LDMP-RENet.

<div><p>Given their fast generalization capability for unseen classes and segmentation ability at pixel scale, models based on few-shot segmentation perform well in solving data insuﬃciency problems during metal defect detection and in delineating refined objects under industrial scenari...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Jiyan Zhang (460271) (author)
مؤلفون آخرون:	Hanze Ding (20890307) (author), Zhangkai Wu (20890310) (author), Ming Peng (553908) (author), Yanfang Liu (306148) (author)
منشور في:	2025
الموضوعات:	Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified prototype reasoning module prototype excitation module fast generalization capability delineating refined objects metal surface defects work (< b ulti -< b r </ b net </ b l </ b class difference </ thereby creating pixel extant researches fail metal surface defect metal defect detection semantic difference </ distortion difference </ information fusion module div >< p extract local descriptors e </ b p </ b embedded local features class differences </ query set pairs </ b query set metal samples defect datasets class differences local information view features pixel scale extant benchmarks e ., semantic intra distortion intra local views view relevance view guidance unseen classes thorough experiments segment precisely relational structure proposed network new state level masks learned prototypes internal factors industrial scenarios graph space global information feature space external factors assess relevance
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Pipeline working mode based on LDMP-RENet.

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