{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T17:53:09Z","timestamp":1768413189280,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Natural Science Basis Research Plan in Shaanxi Province of China","award":["2022JQ-568"],"award-info":[{"award-number":["2022JQ-568"]}]},{"name":"The Natural Science Basis Research Plan in Shaanxi Province of China","award":["21JK0661"],"award-info":[{"award-number":["21JK0661"]}]},{"name":"The Natural Science Basis Research Plan in Shaanxi Province of China","award":["20220133"],"award-info":[{"award-number":["20220133"]}]},{"name":"The Natural Science Basis Research Plan in Shaanxi Province of China","award":["22GXFW0041"],"award-info":[{"award-number":["22GXFW0041"]}]},{"name":"The Natural Science Basis Research Plan in Shaanxi Province of China","award":["2022KJXX-41"],"award-info":[{"award-number":["2022KJXX-41"]}]},{"name":"The Natural Science Basis Research Plan in Shaanxi Province of China","award":["S2208100.W03"],"award-info":[{"award-number":["S2208100.W03"]}]},{"name":"The state key laboratory open project of China National Heavy Machinery Research Institute","award":["2022JQ-568"],"award-info":[{"award-number":["2022JQ-568"]}]},{"name":"The state key laboratory open project of China National Heavy Machinery Research Institute","award":["21JK0661"],"award-info":[{"award-number":["21JK0661"]}]},{"name":"The state key laboratory open project of China National Heavy Machinery Research Institute","award":["20220133"],"award-info":[{"award-number":["20220133"]}]},{"name":"The state key laboratory open project of China National Heavy Machinery Research Institute","award":["22GXFW0041"],"award-info":[{"award-number":["22GXFW0041"]}]},{"name":"The state key laboratory open project of China National Heavy Machinery Research Institute","award":["2022KJXX-41"],"award-info":[{"award-number":["2022KJXX-41"]}]},{"name":"The state key laboratory open project of China National Heavy Machinery Research Institute","award":["S2208100.W03"],"award-info":[{"award-number":["S2208100.W03"]}]},{"name":"Scientific Research Program Funded by Shaanxi Provincial Education Department","award":["2022JQ-568"],"award-info":[{"award-number":["2022JQ-568"]}]},{"name":"Scientific Research Program Funded by Shaanxi Provincial Education Department","award":["21JK0661"],"award-info":[{"award-number":["21JK0661"]}]},{"name":"Scientific Research Program Funded by Shaanxi Provincial Education Department","award":["20220133"],"award-info":[{"award-number":["20220133"]}]},{"name":"Scientific Research Program Funded by Shaanxi Provincial Education Department","award":["22GXFW0041"],"award-info":[{"award-number":["22GXFW0041"]}]},{"name":"Scientific Research Program Funded by Shaanxi Provincial Education Department","award":["2022KJXX-41"],"award-info":[{"award-number":["2022KJXX-41"]}]},{"name":"Scientific Research Program Funded by Shaanxi Provincial Education Department","award":["S2208100.W03"],"award-info":[{"award-number":["S2208100.W03"]}]},{"name":"Young Talent Fund of Association for Science and Technology in Shaanxi, China","award":["2022JQ-568"],"award-info":[{"award-number":["2022JQ-568"]}]},{"name":"Young Talent Fund of Association for Science and Technology in Shaanxi, China","award":["21JK0661"],"award-info":[{"award-number":["21JK0661"]}]},{"name":"Young Talent Fund of Association for Science and Technology in Shaanxi, China","award":["20220133"],"award-info":[{"award-number":["20220133"]}]},{"name":"Young Talent Fund of Association for Science and Technology in Shaanxi, China","award":["22GXFW0041"],"award-info":[{"award-number":["22GXFW0041"]}]},{"name":"Young Talent Fund of Association for Science and Technology in Shaanxi, China","award":["2022KJXX-41"],"award-info":[{"award-number":["2022KJXX-41"]}]},{"name":"Young Talent Fund of Association for Science and Technology in Shaanxi, China","award":["S2208100.W03"],"award-info":[{"award-number":["S2208100.W03"]}]},{"name":"Xi\u2019an Science and Technology Plan Project","award":["2022JQ-568"],"award-info":[{"award-number":["2022JQ-568"]}]},{"name":"Xi\u2019an Science and Technology Plan Project","award":["21JK0661"],"award-info":[{"award-number":["21JK0661"]}]},{"name":"Xi\u2019an Science and Technology Plan Project","award":["20220133"],"award-info":[{"award-number":["20220133"]}]},{"name":"Xi\u2019an Science and Technology Plan Project","award":["22GXFW0041"],"award-info":[{"award-number":["22GXFW0041"]}]},{"name":"Xi\u2019an Science and Technology Plan Project","award":["2022KJXX-41"],"award-info":[{"award-number":["2022KJXX-41"]}]},{"name":"Xi\u2019an Science and Technology Plan Project","award":["S2208100.W03"],"award-info":[{"award-number":["S2208100.W03"]}]},{"name":"Shaanxi Province Innovative Talent Promotion Plant","award":["2022JQ-568"],"award-info":[{"award-number":["2022JQ-568"]}]},{"name":"Shaanxi Province Innovative Talent Promotion Plant","award":["21JK0661"],"award-info":[{"award-number":["21JK0661"]}]},{"name":"Shaanxi Province Innovative Talent Promotion Plant","award":["20220133"],"award-info":[{"award-number":["20220133"]}]},{"name":"Shaanxi Province Innovative Talent Promotion Plant","award":["22GXFW0041"],"award-info":[{"award-number":["22GXFW0041"]}]},{"name":"Shaanxi Province Innovative Talent Promotion Plant","award":["2022KJXX-41"],"award-info":[{"award-number":["2022KJXX-41"]}]},{"name":"Shaanxi Province Innovative Talent Promotion Plant","award":["S2208100.W03"],"award-info":[{"award-number":["S2208100.W03"]}]},{"name":"The Open Project of State Key Laboratory of Metal Extrusion and Forging Equipment Technology","award":["2022JQ-568"],"award-info":[{"award-number":["2022JQ-568"]}]},{"name":"The Open Project of State Key Laboratory of Metal Extrusion and Forging Equipment Technology","award":["21JK0661"],"award-info":[{"award-number":["21JK0661"]}]},{"name":"The Open Project of State Key Laboratory of Metal Extrusion and Forging Equipment Technology","award":["20220133"],"award-info":[{"award-number":["20220133"]}]},{"name":"The Open Project of State Key Laboratory of Metal Extrusion and Forging Equipment Technology","award":["22GXFW0041"],"award-info":[{"award-number":["22GXFW0041"]}]},{"name":"The Open Project of State Key Laboratory of Metal Extrusion and Forging Equipment Technology","award":["2022KJXX-41"],"award-info":[{"award-number":["2022KJXX-41"]}]},{"name":"The Open Project of State Key Laboratory of Metal Extrusion and Forging Equipment Technology","award":["S2208100.W03"],"award-info":[{"award-number":["S2208100.W03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Different types of surface defects will occur during the production of strip steel. To ensure production quality, it is essential to classify these defects. Our research indicates that two main problems exist in the existing strip steel surface defect classification methods: (1) they cannot solve the problem of unbalanced data using few-shot in reality, (2) they cannot meet the requirement of online real-time classification. To solve the aforementioned problems, a relational knowledge distillation self-adaptive residual shrinkage network (RKD-SARSN) is presented in this work. First, the data enhancement strategy of Cycle GAN defective sample migration is designed. Second, the self-adaptive residual shrinkage network (SARSN) is intended as the backbone network for feature extraction. An adaptive loss function based on accuracy and geometric mean (Gmean) is proposed to solve the problem of unbalanced samples. Finally, a relational knowledge distillation model (RKD) is proposed, and the functions of GUI operation interface encapsulation are designed by combining image processing technology. SARSN is used as a teacher model, its generalization performance is transferred to the lightweight network ResNet34, and it is conveniently deployed as a student model. The results show that the proposed method can improve the deployment efficiency of the model and ensure the real-time performance of the classification algorithms. It is superior to other mainstream algorithms for fine-grained images with unbalanced data classification.<\/jats:p>","DOI":"10.3390\/a16110516","type":"journal-article","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T02:46:47Z","timestamp":1699843607000},"page":"516","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Research on a Classification Method for Strip Steel Surface Defects Based on Knowledge Distillation and a Self-Adaptive Residual Shrinkage Network"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9996-711X","authenticated-orcid":false,"given":"Xinbo","family":"Huang","sequence":"first","affiliation":[{"name":"Electronic Information School, Xi\u2019an Polytechnic University, Xi\u2019an 710048, China"},{"name":"College of Mechanical and Electrical Engineering, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Zhiwei","family":"Song","sequence":"additional","affiliation":[{"name":"Electronic Information School, Xi\u2019an Polytechnic University, Xi\u2019an 710048, China"}]},{"given":"Chao","family":"Ji","sequence":"additional","affiliation":[{"name":"Electronic Information School, Xi\u2019an Polytechnic University, Xi\u2019an 710048, China"}]},{"given":"Ye","family":"Zhang","sequence":"additional","affiliation":[{"name":"Electronic Information School, Xi\u2019an Polytechnic University, Xi\u2019an 710048, China"}]},{"given":"Luya","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6957","DOI":"10.1109\/TIP.2021.3100556","article-title":"Interleaved Deep Artifacts-Aware Attention Mechanism for Concrete Structural Defect Classification","volume":"30","author":"Bhattacharya","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6055","DOI":"10.1109\/TSP.2020.3031188","article-title":"Defect Detection and Classification by Training a Generic Convolutional Neural Network Encoder","volume":"68","author":"Dong","year":"2020","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1109\/JSTARS.2020.3031918","article-title":"Analysis and Classification of SAR Textures Using Information Theory","volume":"14","author":"Chagas","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s42243-020-00501-1","article-title":"Multi-class classification method for steel surface defects with feature noise","volume":"28","author":"Chu","year":"2021","journal-title":"J. Iron Steel Res. Int."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"115857","DOI":"10.1109\/ACCESS.2020.3004473","article-title":"An SVM-Based AdaBoost Cascade Classifier for Sonar Image","volume":"8","author":"Xu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3709","DOI":"10.1109\/JBHI.2021.3052916","article-title":"Synergic Adversarial Label Learning for Grading Retinal Diseases via Knowledge Distillation and Multi-task Learning","volume":"25","author":"Ju","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.neunet.2021.01.005","article-title":"Improved deep CNNs based on Nonlinear Hybrid Attention Module for image classification","volume":"140","author":"Guo","year":"2021","journal-title":"Neural Netw."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1109\/TSM.2021.3118922","article-title":"Applying Data Augmentation and Mask R-CNN-Based Instance Segmentation Method for Mixed-Type Wafer Maps Defect Patterns Classification","volume":"34","author":"Chiu","year":"2021","journal-title":"IEEE Trans. Semicond. Manuf."},{"key":"ref_9","first-page":"1","article-title":"Real-Time Defect Detection of Track Components: Considering Class Imbalance and Subtle Difference Between Classes","volume":"70","author":"Tu","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1109\/TPAMI.2022.3153611","article-title":"Data set Bias in Few-shot Image Recognition","volume":"45","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lv, N., Ma, H., Chen, C., Pei, Q., Zhou, Y., Xiao, F., and Li, J. (2021, January 11\u201316). Remote Sensing Data Augmentation Through Adversarial Training. Proceedings of the IGARSS 2020\u20142020 IEEE International Geoscience and Remote Sensing Symposium, Brussels, Belgium.","DOI":"10.1109\/IGARSS39084.2020.9324263"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1109\/TCBB.2007.070207","article-title":"On the Classification of a Small Imbalanced Cytogenetic Image Database","volume":"4","author":"Lerner","year":"2007","journal-title":"IEEE ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1109\/TPAMI.2019.2929166","article-title":"Multiset Feature Learning for Highly Imbalanced Data Classification","volume":"43","author":"Jing","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"8773","DOI":"10.1109\/JSTARS.2021.3109012","article-title":"Attention Multisource Fusion-Based Deep Few-Shot Learning for Hyperspectral Image Classification","volume":"14","author":"Liang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, K., Wang, X., and Ji, L. (2019, January 16\u201318). Application of Multi-Scale Feature Fusion and Deep Learning in Detection of Steel Strip Surface Defect. Proceedings of the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM), Dublin, Ireland.","DOI":"10.1109\/AIAM48774.2019.00136"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"171240","DOI":"10.1109\/ACCESS.2020.3024582","article-title":"Fault Location of Strip Steel Surface Quality Defects on Hot-Rolling Production Line Based on Information Fusion of Historical Cases and Process Data","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_17","first-page":"2672","article-title":"Generative Adversarial Networks","volume":"3","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"96349","DOI":"10.1109\/ACCESS.2019.2929270","article-title":"Liver Semantic Segmentation Algorithm Based on Improved Deep Adversarial Networks in combination of Weighted Loss Function on Abdominal CT Images","volume":"7","author":"Xia","year":"2019","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","article-title":"Generative Adversarial Networks: An Overview","volume":"35","author":"Creswell","year":"2018","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_20","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 2016 IEEE conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2016, January 27\u201330). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4681","DOI":"10.1109\/TII.2019.2943898","article-title":"Deep Residual Shrinkage Networks for Fault Diagnosis","volume":"16","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_23","first-page":"2011","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"Jie","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Verma, A., Sharma, M., Hebbalaguppe, R., Hassan, E., and Vig, L. (2016, January 18\u201320). Automatic Container Code Recognition via Spatial Transformer Networks and Connected Component Region Proposals. Proceedings of the IEEE International Conference on Machine Learning and Applications, Anaheim, CA, USA.","DOI":"10.1109\/ICMLA.2016.0130"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3261","DOI":"10.1109\/TII.2018.2819674","article-title":"Sparse deep stacking network for fault diagnosis of motor","volume":"14","author":"Sun","year":"2018","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, Y., Bai, Y., Zhang, W., and Mei, T. (2019, January 15\u201320). Destruction and Construction Learning for Fine-Grained Image Recognition. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00530"},{"key":"ref_28","unstructured":"Leng, R., and Zhou, W. (2016, January 27\u201328). Optimization Research and Application of Unbalanced Data Set Multi-classification Algorithm. Proceedings of the International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Raj, V., Magg, S., and Wermter, S. (2016, January 28\u201330). Towards effective classification of imbalanced data with convolutional neural networks. Proceedings of the IAPR Workshop on Artificial Neural Networks in Pattern Recognition, Ulm, Germany.","DOI":"10.1007\/978-3-319-46182-3_13"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Berta, R., and De Gloria, A. (2023). Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2022, Springer. Lecture Notes in Electrical Engineering.","DOI":"10.1007\/978-3-031-30333-3"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2021","DOI":"10.1109\/JBHI.2018.2875812","article-title":"Gait Evaluation Using Procrustes and Euclidean Distance Matrix Analysis","volume":"23","author":"Anwary","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_32","first-page":"38","article-title":"Distilling the Knowledge in a Neural Network","volume":"14","author":"Hinton","year":"2015","journal-title":"Comput. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Park, W., Kim, D., Lu, Y., and Cho, M. (2019, January 15\u201320). Relational Knowledge Distillation. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00409"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Angelopoulos, A., Michailidis, E.T., Nomikos, N., Trakadas, P., Hatziefremidis, A., Voliotis, S., and Zahariadis, T. (2019). Tackling faults in the industry 4.0 era\u2014A survey of machine-learning solutions and key aspects. Sensors, 20.","DOI":"10.3390\/s20010109"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1833","DOI":"10.1007\/s10845-020-01670-2","article-title":"A steel surface defect inspection approach towards smart industrial monitoring","volume":"32","author":"Hao","year":"2021","journal-title":"J. Intell. Manuf."},{"key":"ref_36","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 Process."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Czimmermann, T., Ciuti, G., Milazzo, M., Chiurazzi, M., Roccella, S., Oddo, C.M., and Dario, P. (2020). Visual-based defect detection and classification approaches for industrial applications\u2014A survey. Sensors, 20.","DOI":"10.3390\/s20051459"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"9713","DOI":"10.1109\/TII.2023.3234030","article-title":"Attention guided multi-task learning for surface defect identification","volume":"19","author":"Sampath","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2050010","DOI":"10.1142\/S0219467820500102","article-title":"Multi-Scale Fractional Tonal Correction Bilateral Filter-Based Hazy Image Enhancement","volume":"20","author":"Nnolim","year":"2020","journal-title":"Int. J. Image Graph."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"8881","DOI":"10.1007\/s10489-021-02197-6","article-title":"Automatic coronary artery segmentation algorithm based on deep learning and digital image processing","volume":"51","author":"Tian","year":"2021","journal-title":"Appl. Intell."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/11\/516\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:21:12Z","timestamp":1760131272000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/11\/516"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,10]]},"references-count":40,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["a16110516"],"URL":"https:\/\/doi.org\/10.3390\/a16110516","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,10]]}}}