Semiconductor Optoelectronics, Volume. 46, Issue 1, 90(2025)

Classification of Steel Surface Defects Based on Multi-Attention Mechanism

YING Hejie1,2, LAI Lianfeng1,2, REN Xuehang1, XIONG Lingling3, and XUE Zhangqi1
Author Affiliations
  • 1College of Mechanical and Electrical Engineering Ningde Normal University, Ningde 352100, CHN
  • 2The Collaborative Innovation Center of Ningde Normal University Ningde Normal University, Ningde 352100, CHN
  • 3College of Information Engineering Ningde Normal University, Ningde 352100, CHN
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    References(14)

    [3] [3] Zhang J, Li S, Yan Y, et al. Surface defect classification of steel strip with few samples based on dual-stream neural network[J]. Steel Research International, 2022, 93(5): 2100554.

    [6] [6] Samsudin S S, Arof H, Harun S W, et al. Steel surface defect classification using multi-resolution empirical mode decomposition and LBP[J]. Measurement Science and Technology, 2021, 32(1): 015601.

    [8] [8] Zhang Z F, Liu W, Ostrosi E, et al. Steel strip surface inspection through the combination of feature selection and multiclass classifiers[J]. Engineering Computations, 2021, 38(4): 1831-1850.

    [9] [9] Bao Y, Song K, Liu J, et al. Triplet-graph reasoning network for few-shot metal generic surface defect segmentation[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 5011111.

    [11] [11] Tang B, Chen L, Sun W, et al. Review of surface defect detection of steel products based on machine vision[J]. IET Image Processing, 2023, 17(2): 303-322.

    [13] [13] Demir K, Ay M, Cavas M, et al. Automated steel surface defect detection and classification using a new deep learning-based approach[J]. Neural Computing and Applications, 2023, 35(11): 8389-8406.

    [14] [14] Zhao Y, Sun X, Yang J. Automatic recognition of surface defects of hot rolled strip steel based on deep parallel attention convolution neural network[J]. Materials Letters, 2023, 353: 135313.

    [15] [15] Liu Y, Yuan Y, Balta C, et al. A light-weight deep-learning model with multi-scale features for steel surface defect classification[J]. Materials, 2020, 13(20): 4629.

    [16] [16] Jain S, Seth G, Paruthi A, et al. Synthetic data augmentation for surface defect detection and classification using deep learning[J]. Journal of Intelligent Manufacturing, 2022, 33(4): 1007-1020.

    [17] [17] Feng X, Luo L, Gao X. SDDA: A method of surface defect data augmentation of hot-rolled strip steel[J]. Journal of Electronic Imaging, 2022, 31(3): 033002.

    [20] [20] Sun Y, Xia C, Gao X, et al. Aggregating dense and attentional multi-scale feature network for salient object detection[J]. Digital Signal Processing, 2022, 130: 103747.

    [21] [21] Lin T Y, RoyChowdhury A, Maji S. Bilinear CNN models for fine-grained visual recognition[C]//2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015: 1449-1457.

    [22] [22] Gao Y, Beijbom O, Zhang N, et al. Compact bilinear pooling[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016: 317-326.

    [23] [23] Woo S, Park J, Lee J Y, et al. CBAM: Convolutional block attention module[C]//Computer Vision-ECCV 2018. Cham: Springer International Publishing, 2018: 3-19.

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    YING Hejie, LAI Lianfeng, REN Xuehang, XIONG Lingling, XUE Zhangqi. Classification of Steel Surface Defects Based on Multi-Attention Mechanism[J]. Semiconductor Optoelectronics, 2025, 46(1): 90

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    Paper Information

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    Received: Jul. 17, 2024

    Accepted: Sep. 18, 2025

    Published Online: Sep. 18, 2025

    The Author Email:

    DOI:10.16818/j.issn1001-5868.20240717001

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