CAT-EDNet: Cross-Attention Transformer-Based Encoder-Decoder Network for Salient Defect Detection of Strip Steel Surface

The morphologies of various surface defects on strip steel suffer from oil stain, water drops, steel textures, and erratic illumination. It is still challenging to recognize defect boundary precisely from cluttered backgrounds. This article emphasizes such a fact that skip connections between encode...

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Veröffentlicht in:	IEEE transactions on instrumentation and measurement Jg. 71; S. 1 - 13
Hauptverfasser:	Luo, Qiwu, Su, Jiaojiao, Yang, Chunhua, Gui, Weihua, Silven, Olli, Liu, Li
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Automated visual inspection (AVI) Coders Decoding encoder–decoder network (EDNet) Entropy (Information theory) Feature extraction Inspection salient detection Steel steel strip Strip steel Strips Surface defects transformer Transformers Visualization Water drops
ISSN:	0018-9456, 1557-9662
Online-Zugang:	Volltext
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