Recognition Algorithm of Strip Steel Surface Defects Based on Attention Model

Yanuo Lu; Bingcai Chen; Degang Chen; Shixiang Yan; Shunping Li

doi:10.3788/LOP202158.1410014

[1] Wang Y P. The principle and technology of plate and strip production[M], 3-5(1995).

[2] Li D, Bai G J, Jin Y Y et al. Machine-vision based defect detection algorithm for packaging bags[J]. Laser & Optoelectronics Progress, 56, 091501(2019).

[3] Hu L T. Research on surface defect recognition of steel plate based on machine vision[D](2018).

[4] Guo H, Xu W, Liu Y F. Steel plate surface defect recognition based on support vector machine[J]. Journal of Donghua University (Natural Science), 44, 635-639(2018).

[5] Shan D R, Tong C, Nai X S et al. Recognition of surface defects on strip based on wavelet and gray levelco-occurrence matrix[J]. Manufacturing Technology & Machine Tool, 120-123(2020).

[6] Chang J, Guan S Q, Shi H Y et al. Strip defect classification based on the improved generative adversarial networks and MobileNetV3[J]. Laser & Optoelectronics Progress, 58, 0410016(2021).

[7] Chen X F, Yu F Q, Chen Y. Application of improved MB-LBP features and LPP algorithm in strip surface defect identification[J]. Transducer and Microsystem Technologies, 39, 156-160(2020).

[8] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 770-778(2016).

[9] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 7132-7141(2018).

[10] Xu W H, Pei Y J, Gao D L et al. Mass classification of breast mammogram based on attention mechanism and transfer learning[J]. Laser & Optoelectronics Progress, 58, 0410007(2021).

[11] Pu X F, Ning Q, Lei Y J et al. Identification of agricultural plant diseases based on binarized convolutional neural network[J]. Journal of Chinese Agricultural Mechanization, 41, 177-182(2020).

[12] Vaswani A, Shazeer N, Parmar N et al. Attention is all you need[C]. //Advances in neural information processing systems, December 4-9, 2017, Long Beach, CA, USA, 5998-6008(2017).

[13] Nair V, Hinton G E. Rectified linear units improve restricted boltzmann machines[C]. //In Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21, 2010, Madison, WI, United States, 807-814(2010).

[14] Pan S J, Yang Q. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 22, 1345-1359(2010).

[15] Shao L, Zhu F, Li X L. Transfer learning for visual categorization: a survey[J]. IEEE Transactions on Neural Networks and Learning Systems, 26, 1019-1034(2015). http://ieeexplore.ieee.org/document/6847217

[16] Donahue J, Jia Y Q, Vinyals O et al. DeCAF: a deep convolutional activation feature for generic visual recognition[C]. //Proceedings of the 31st International Conference on Machine Learning, June 21, 2014, Beijing, China, 32, 647-655(2014).

[17] Song K C, Yan Y H. A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects[J]. Applied Surface Science, 285, 858-864(2013). http://www.sciencedirect.com/science/article/pii/S0169433213016437

[18] Liu W. Analysis and realization of medical images pseudo color based on the regional segmentation[J]. Journal of Minzu University of China (Natural Sciences Edition), 28, 61-64(2019).