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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Published in:	IEEE transactions on instrumentation and measurement Vol. 71; pp. 1 - 13
Main Authors:	Luo, Qiwu, Su, Jiaojiao, Yang, Chunhua, Gui, Weihua, Silven, Olli, Liu, Li
Format:	Journal Article
Language:	English
Published:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
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Abstract	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 encoder and decoder are not equally effective, attempts to adaptively allocate the aggregation weights that represent differentiated information entropy values in channelwise, by importing a stack of cross-attention transformer (CAT) into the encoder-decoder network (EDNet). Besides, a cross-attention refinement module (CARM) is constructed closely after the decoder to further optimize the coarse saliency maps. This newly nominated CAT-EDNet can well address the semantic gap issue among the multiscale features for its multihead attention structure. The CAT-EDNet performs best on insuring defect integrity and maintaining defect boundary details when compared with 12 state-of-the-arts, and the detection efficiency is at 28 fps even under the noise interfered scenario.
AbstractList	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 encoder and decoder are not equally effective, attempts to adaptively allocate the aggregation weights that represent differentiated information entropy values in channelwise, by importing a stack of cross-attention transformer (CAT) into the encoder-decoder network (EDNet). Besides, a cross-attention refinement module (CARM) is constructed closely after the decoder to further optimize the coarse saliency maps. This newly nominated CAT-EDNet can well address the semantic gap issue among the multiscale features for its multihead attention structure. The CAT-EDNet performs best on insuring defect integrity and maintaining defect boundary details when compared with 12 state-of-the-arts, and the detection efficiency is at 28 fps even under the noise interfered scenario.
Author	Gui, Weihua Silven, Olli Liu, Li Su, Jiaojiao Luo, Qiwu Yang, Chunhua
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References	ref13 Vaswani (ref15); 30 ref35 ref12 ref34 ref37 ref14 ref36 ref31 ref30 ref11 ref33 ref10 ref2 ref1 ref17 Chen (ref18) 2021 ref39 ref16 Xie (ref19) 2021 ref24 ref45 ref26 ref25 ref20 ref42 ref41 ref22 ref44 ref21 ref43 Islam (ref23) ref28 ref27 ref29 ref8 ref7 Nagendar (ref38) ref9 ref4 ref3 ref6 ref5 ref40 Tieleman (ref32) 2012; 4
References_xml	– ident: ref2 doi: 10.1109/TIM.2019.2963555 – ident: ref34 doi: 10.1109/CVPR.2014.39 – ident: ref26 doi: 10.1109/CVPR.2019.00766 – ident: ref27 doi: 10.1609/aaai.v36i3.20144 – ident: ref4 doi: 10.1109/CVPR.2009.5206596 – ident: ref7 doi: 10.1109/TIM.2012.2218677 – ident: ref22 doi: 10.1109/CVPR.2017.189 – volume: 4 start-page: 26 issue: 2 year: 2012 ident: ref32 article-title: RMSprop: Divide the gradient by a running average of its recent magnitude publication-title: Neural Netw. Mach. Learn. – ident: ref41 doi: 10.1109/CVPR.2019.00403 – ident: ref10 doi: 10.1109/ICCV.2019.00736 – ident: ref29 doi: 10.1007/978-3-319-50835-1_22 – ident: ref13 doi: 10.1109/CVPR46437.2021.00865 – ident: ref17 doi: 10.1109/CVPR46437.2021.00681 – ident: ref31 doi: 10.1109/ICCV.2015.123 – ident: ref9 doi: 10.1109/CVPR.2015.7299184 – ident: ref43 doi: 10.1109/CVPR42600.2020.00285 – volume: 30 start-page: 5998 volume-title: Proc. NIPS ident: ref15 article-title: Attention is all you need – ident: ref33 doi: 10.1109/ICCV.2017.487 – ident: ref42 doi: 10.1109/ICCV.2019.00887 – volume-title: Proc. BMVC ident: ref38 article-title: Neuro-IoU: Learning a surrogate loss for semantic segmentation – ident: ref37 doi: 10.1007/978-3-319-75238-9_6 – ident: ref11 doi: 10.1109/TIP.2021.3072811 – ident: ref12 doi: 10.1016/j.patcog.2020.107404 – ident: ref39 doi: 10.1109/CVPR.2018.00326 – ident: ref6 doi: 10.1109/TIM.2019.2915404 – ident: ref36 doi: 10.24963/ijcai.2018/97 – ident: ref44 doi: 10.1109/CVPR.2019.00320 – ident: ref40 doi: 10.1109/CVPR.2019.00404 – ident: ref45 doi: 10.1109/TIP.2020.2965989 – ident: ref8 doi: 10.1109/TPAMI.2016.2562626 – ident: ref30 doi: 10.1109/ACSSC.2003.1292216 – ident: ref35 doi: 10.1109/CVPR.2012.6247743 – ident: ref20 doi: 10.1007/978-3-030-58452-8_13 – ident: ref24 doi: 10.1109/ICCV.2017.433 – ident: ref1 doi: 10.1109/TIM.2020.3030167 – year: 2021 ident: ref19 article-title: SegFormer: Simple and efficient design for semantic segmentation with transformers publication-title: arXiv:2105.15203 – start-page: 1 volume-title: Proc. BMVC ident: ref23 article-title: Salient object detection using a context-aware refinement network – ident: ref25 doi: 10.1109/CVPR.2018.00187 – ident: ref5 doi: 10.1109/TII.2019.2958826 – ident: ref16 doi: 10.1109/TNNLS.2022.3227717 – ident: ref21 doi: 10.1109/ICCV48922.2021.00683 – ident: ref28 doi: 10.1007/s10479-005-5724-z – year: 2021 ident: ref18 article-title: TransUNet: Transformers make strong encoders for medical image segmentation publication-title: arXiv:2102.04306 – ident: ref3 doi: 10.1109/TIM.2018.2852918 – ident: ref14 doi: 10.1109/TIM.2020.3002277
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Snippet	The morphologies of various surface defects on strip steel suffer from oil stain, water drops, steel textures, and erratic illumination. It is still...
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SubjectTerms	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
Title	CAT-EDNet: Cross-Attention Transformer-Based Encoder-Decoder Network for Salient Defect Detection of Strip Steel Surface
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