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of Technology","award":["2024JC-YBQN-0697"],"award-info":[{"award-number":["2024JC-YBQN-0697"]}]},{"name":"Doctoral Scientific Research Startup Foundation of Xi\u2019an University of Technology","award":["23JK0387"],"award-info":[{"award-number":["23JK0387"]}]},{"name":"Doctoral Scientific Research Startup Foundation of Xi\u2019an University of Technology","award":["103-451123015"],"award-info":[{"award-number":["103-451123015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Strip steel plays a crucial role in modern industrial production, where enhancing the accuracy and real-time capabilities of surface defect classification is essential. However, acquiring and annotating defect samples for training deep learning models are challenging, further complicated by the presence of redundant information in these samples. These issues hinder the classification of strip steel surface defects. To address these challenges, this paper introduces a high real-time network, ODNet (Orthogonal Decomposition Network), designed for few-shot strip steel surface defect classification. ODNet utilizes ResNet as its backbone and incorporates orthogonal decomposition technology to reduce the feature redundancies. Furthermore, it integrates skip connection to preserve essential correlation information in the samples, preventing excessive elimination. The model optimizes the parameter efficiency by employing Euclidean distance as the classifier. The orthogonal decomposition not only helps reduce redundant image information but also ensures compatibility with the Euclidean distance requirement for orthogonal input. Extensive experiments conducted on the FSC-20 benchmark demonstrate that ODNet achieves superior real-time performance, accuracy, and generalization compared to alternative methods, effectively addressing the challenges of few-shot strip steel surface defect classification.<\/jats:p>","DOI":"10.3390\/s24144630","type":"journal-article","created":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T15:15:19Z","timestamp":1721229319000},"page":"4630","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["ODNet: A High Real-Time Network Using Orthogonal Decomposition for Few-Shot Strip Steel Surface Defect Classification"],"prefix":"10.3390","volume":"24","author":[{"given":"He","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Automation and Information Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6618-1380","authenticated-orcid":false,"given":"Han","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Automation and Information Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7426-3212","authenticated-orcid":false,"given":"Runyuan","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Automation and Information Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lili","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Automation and Information Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0120-7102","authenticated-orcid":false,"given":"Qing","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Automation and Information Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenlu","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Shannxi Xueqian Normal University, Xi\u2019an 710100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cumbajin, E., Rodrigues, N., Costa, P., Miragaia, R., Fraz\u00e3o, L., Costa, N., Fern\u00e1ndez-Caballero, A., Carneiro, J., Buruberri, L.H., and Pereira, A. (2023). A systematic review on deep learning with CNNs applied to surface defect detection. J. Imaging, 9.","DOI":"10.3390\/jimaging9100193"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ma, J., Zhang, T., Yang, C., Cao, Y., Xie, L., Tian, H., and Li, X. (2023). Review of Wafer Surface Defect Detection Methods. Electronics, 12.","DOI":"10.3390\/electronics12081787"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"12131","DOI":"10.1007\/s10462-023-10475-7","article-title":"A survey of real-time surface defect inspection methods based on deep learning","volume":"56","author":"Liu","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1049\/ipr2.12647","article-title":"Review of surface defect detection of steel products based on machine vision","volume":"17","author":"Tang","year":"2023","journal-title":"IET Image Processing"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7842","DOI":"10.1109\/TII.2024.3366221","article-title":"MINet: Multiscale Interactive Network for Real-Time Salient Object Detection of Strip Steel Surface Defects","volume":"20","author":"Shen","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_6","first-page":"5010515","article-title":"Generative and Contrastive Combined Support Sample Synthesis Model for Few\/Zero-Shot Surface Defect Recognition","volume":"73","author":"Dong","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"105262","DOI":"10.1016\/j.autcon.2023.105262","article-title":"3D vision technologies for a self-developed structural external crack damage recognition robot","volume":"159","author":"Hu","year":"2024","journal-title":"Autom. Constr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3398","DOI":"10.1109\/JSEN.2020.3024753","article-title":"A new defect classification approach based on the fusion matrix of multi-eigenvalue","volume":"21","author":"Lei","year":"2020","journal-title":"IEEE Sensors J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"035601","DOI":"10.1088\/1361-6501\/ac41a6","article-title":"Deep learning model for imbalanced multi-label surface defect classification","volume":"33","author":"Liu","year":"2021","journal-title":"Meas. Sci. Technol."},{"key":"ref_10","first-page":"1","article-title":"Attention guided multi-task learning for surface defect identification","volume":"99","author":"Sampath","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Feng, X., Gao, X., and Luo, L. (2021). A ResNet50-based method for classifying surface defects in hot-rolled strip steel. Mathematics, 9.","DOI":"10.3390\/math9192359"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, H., Sun, Q., and Xu, K. (2023). A Self-Supervised Model Based on CutPaste-Mix for Ductile Cast Iron Pipe Surface Defect Classification. Sensors, 23.","DOI":"10.3390\/s23198243"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chen, G., Yu, H., Jiang, L., and Shang, H. (2021, January 23\u201325). Few-Shot Learning on 3D Surface Defect Detection with PM Networks. Proceedings of the 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture, Manchester, UK.","DOI":"10.1145\/3495018.3495037"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.jmsy.2023.06.016","article-title":"Online visual end-to-end detection monitoring on surface defect of aluminum strip under the industrial few-shot condition","volume":"70","author":"Ma","year":"2023","journal-title":"J. Manuf. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.ifacol.2018.09.412","article-title":"Real-time detection of steel strip surface defects based on improved yolo detection network","volume":"51","author":"Li","year":"2018","journal-title":"IFAC-PapersOnLine"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wu, S., Zhao, S., Zhang, Q., Chen, L., and Wu, C. (2021). Steel Surface defect classification based on small sample learning. Appl. Sci., 11.","DOI":"10.3390\/app112311459"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wan, S., Guan, S., and Tang, Y. (2023). Advancing bridge structural health monitoring: Insights into knowledge-driven and data-driven approaches. J. Data Sci. Intell. Syst.","DOI":"10.47852\/bonviewJDSIS3202964"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Guo, R., Chen, Q., Liu, H., and Wang, W. (2024). Adversarial Robustness Enhancement for Deep Learning-Based Soft Sensors: An Adversarial Training Strategy Using Historical Gradients and Domain Adaptation. Sensors, 24.","DOI":"10.3390\/s24123909"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"101813","DOI":"10.1016\/j.aei.2022.101813","article-title":"Zero-shot surface defect recognition with class knowledge graph","volume":"54","author":"Li","year":"2022","journal-title":"Adv. Eng. Inform."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"9667","DOI":"10.1109\/TII.2022.3233654","article-title":"Shape-Consistent One-Shot Unsupervised Domain Adaptation for Rail Surface Defect Segmentation","volume":"19","author":"Ma","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5020010","DOI":"10.1109\/TIM.2022.3196447","article-title":"Selective prototype network for few-shot metal surface defect segmentation","volume":"71","author":"Yu","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, Z., Guo, Z., Li, C., Gao, C., and Huang, N. (2021, January 15\u201317). Few-shot Steel Surface Defect Detection Based on Meta Learning. Proceedings of the 2021 10th International Conference on Computing and Pattern Recognition, Shanghai, China.","DOI":"10.1145\/3497623.3497641"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3507","DOI":"10.1007\/s10845-022-02022-y","article-title":"Few-shot defect recognition of metal surfaces via attention-embedding and self-supervised learning","volume":"34","author":"Liu","year":"2023","journal-title":"J. Intell. Manuf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., and Yoo, Y. (2019). CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features. arXiv, Available online: http:\/\/arxiv.org\/abs\/1905.04899.","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, X., Teng, W., and Liu, Y. (2022). A Model-Agnostic Meta-Baseline Method for Few-Shot Fault Diagnosis of Wind Turbines. Sensors, 22.","DOI":"10.3390\/s22093288"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Gomes, J.C., Borges, L.d.A.B., and Borges, D.L. (2023). A Multi-Layer Feature Fusion Method for Few-Shot Image Classification. Sensors, 23.","DOI":"10.3390\/s23156880"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gong, Y., Wang, X., Zhou, C., Ge, M., Liu, C., and Zhang, X. (2024). Human-machine knowledge hybrid augmentation method for surface defect detection based few-data learning. J. Intell. Manuf., 1\u201320.","DOI":"10.1007\/s10845-023-02270-6"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1622","DOI":"10.1109\/TPWRD.2024.3373130","article-title":"DP-GAN: A Transmission Line Bolt Defects Generation Network Based on Dual Discriminator Architecture and Pseudo-Enhancement Strategy","volume":"39","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"015202","DOI":"10.1088\/1361-6501\/ac90de","article-title":"Cross-domain few-shot defect recognition for metal surfaces","volume":"34","author":"Duan","year":"2023","journal-title":"Meas. Sci. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5097","DOI":"10.1007\/s10489-024-05440-y","article-title":"BiLSTM-TANet: An adaptive diverse scenes model with context embeddings for few-shot learning","volume":"54","author":"Zhang","year":"2024","journal-title":"Appl. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1016\/j.promfg.2020.05.146","article-title":"One-shot recognition of manufacturing defects in steel surfaces","volume":"48","author":"Deshpande","year":"2020","journal-title":"Procedia Manuf."},{"key":"ref_33","unstructured":"Finn, C., Abbeel, P., and Levine, S. (2017, January 6\u201311). Model-agnostic meta-learning for fast adaptation of deep networks. Proceedings of the International Conference on Machine Learning, PMLR, Sydney, NSW, Australia."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"113612","DOI":"10.1016\/j.measurement.2023.113612","article-title":"Adaptive-MAML: Few-shot metal surface defects diagnosis based on model-agnostic meta-learning","volume":"223","author":"Pang","year":"2023","journal-title":"Measurement"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.1007\/s40747-023-01219-9","article-title":"Permute-MAML: Exploring industrial surface defect detection algorithms for few-shot learning","volume":"10","author":"Pang","year":"2024","journal-title":"Complex Intell. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"6859","DOI":"10.1109\/TII.2022.3181692","article-title":"A self-interpretable soft sensor based on deep learning and multiple attention mechanism: From data selection to sensor modeling","volume":"19","author":"Guo","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5011111","DOI":"10.1109\/TIM.2021.3083561","article-title":"Triplet-graph reasoning network for few-shot metal generic surface defect segmentation","volume":"70","author":"Bao","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"8072","DOI":"10.1109\/TII.2022.3216900","article-title":"Unseen-material few-shot defect segmentation with optimal bilateral feature transport network","volume":"19","author":"Shan","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2100554","DOI":"10.1002\/srin.202100554","article-title":"Surface Defect Classification of Steel Strip with Few Samples Based on Dual-Stream Neural Network","volume":"93","author":"Zhang","year":"2022","journal-title":"Steel Res. Int."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.2355\/isijinternational.ISIJINT-2021-051","article-title":"Surface Defects Classification of Hot Rolled Strip Based on Few-shot Learning","volume":"62","author":"Wang","year":"2022","journal-title":"ISIJ Int."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yu, J., Liu, K., Qin, L., Li, Q., Zhao, F., Wang, Q., Liu, H., Li, B., Wang, J., and Li, K. (2022). DMnet: A New Few-Shot Framework for Wind Turbine Surface Defect Detection. Machines, 10.","DOI":"10.3390\/machines10060487"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"104172","DOI":"10.1016\/j.jvcir.2024.104172","article-title":"Few-shot defect classification via feature aggregation based on graph neural network","volume":"101","author":"Zhang","year":"2024","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1007\/s10845-023-02080-w","article-title":"A knowledge distillation-based multi-scale relation-prototypical network for cross-domain few-shot defect classification","volume":"35","author":"Zhao","year":"2024","journal-title":"J. Intell. Manuf."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5007410","DOI":"10.1109\/TIM.2023.3246519","article-title":"Cross Position Aggregation Network for Few-Shot Strip Steel Surface Defect Segmentation","volume":"72","author":"Feng","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"121010","DOI":"10.1115\/1.4063356","article-title":"Dual-Metric Neural Network with Attention Guidance for Surface Defect Few-Shot Detection in Smart Manufacturing","volume":"145","author":"Gao","year":"2023","journal-title":"J. Manuf. Sci.-Eng.-Trans. Asme"},{"key":"ref_46","unstructured":"Kang, G.W., and Liu, H.B. (2005, January 18\u201321). Surface defects inspection of cold rolled strips based on neural network. Proceedings of the 2005 International Conference on Machine Learning and Cybernetics, Guangzhou, China."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5797654","DOI":"10.1155\/2016\/5797654","article-title":"Detection of surface defects on steel strips based on singular value decomposition of digital image","volume":"2016","author":"Sun","year":"2016","journal-title":"Math. Probl. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"112446","DOI":"10.1016\/j.measurement.2023.112446","article-title":"FaNet: Feature-aware network for few shot classification of strip steel surface defects","volume":"208","author":"Zhao","year":"2023","journal-title":"Measurement"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1016\/j.apsusc.2013.09.002","article-title":"A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects","volume":"285","author":"Song","year":"2013","journal-title":"Appl. Surf. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Feng, X., Gao, X., and Luo, L. (2021). X-SDD: A new benchmark for hot rolled steel strip surface defects detection. Symmetry, 13.","DOI":"10.3390\/sym13040706"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Lv, X., Duan, F., Jiang, J.j., Fu, X., and Gan, L. (2020). Deep metallic surface defect detection: The new benchmark and detection network. Sensors, 20.","DOI":"10.3390\/s20061562"},{"key":"ref_53","unstructured":"Ziko, I., Dolz, J., Granger, E., and Ayed, I.B. (2020, January 13\u201318). Laplacian regularized few-shot learning. Proceedings of the International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xu, C., Liu, C., Zhang, L., and Fu, Y. (2020, January 13\u201319). Instance credibility inference for few-shot learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01285"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zhang, C., Cai, Y., Lin, G., and Shen, C. (2020, January 13\u201319). Deepemd: Few-shot image classification with differentiable earth mover\u2019s distance and structured classifiers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01222"},{"key":"ref_56","unstructured":"Snell, J., Swersky, K., and Zemel, R. (2017). Prototypical networks for few-shot learning. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_57","unstructured":"Boudiaf, M., Masud, Z., Rony, J., Dolz, J., Piantanida, P., and Ayed, I. (2020). Transductive information maximization for few-shot learning. arXiv."},{"key":"ref_58","unstructured":"Chen, W.Y., Liu, Y.C., Kira, Z., Wang, Y.C.F., and Huang, J.B. (2019). A closer look at few-shot classification. arXiv."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"5010310","DOI":"10.1109\/TIM.2022.3169547","article-title":"Graph embedding and optimal transport for few-shot classification of metal surface defect","volume":"71","author":"Xiao","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/14\/4630\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:18:22Z","timestamp":1760109502000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/14\/4630"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,17]]},"references-count":59,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["s24144630"],"URL":"https:\/\/doi.org\/10.3390\/s24144630","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,17]]}}}