{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T21:45:37Z","timestamp":1772142337366,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T00:00:00Z","timestamp":1675900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["KYCX20_0998"],"award-info":[{"award-number":["KYCX20_0998"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the production process of steel products, it is very important to find defects, which can not only reduce the failure rate of industrial production but also can reduce economic losses. All deep learning-based methods need many labeled samples for training. However, in the industrial field, there is a lack of sufficient training samples, especially in steel surface defects. It is almost impossible to collect enough samples that can be used for training. To solve this kind of problem, different from traditional data enhancement methods, this paper constructed a data enhancement model dependent on GAN, using our designed EDCGAN to generate abundant samples that can be used for training. Finally, we mixed different proportions of the generated samples with the original samples and tested them through the MobileNet V2 classification model. The test results showed that if we added the samples generated by EDCGAN to the original samples, the classification results would gradually improve. When the ratio reaches 80%, the overall classification result reaches the highest, achieving an accuracy rate of more than 99%. The experimental process proves the effectiveness of this method and can improve the quality of steel processing.<\/jats:p>","DOI":"10.3390\/s23041953","type":"journal-article","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T02:09:59Z","timestamp":1675994999000},"page":"1953","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["An End-to-End Steel Surface Classification Approach Based on EDCGAN and MobileNet V2"],"prefix":"10.3390","volume":"23","author":[{"given":"Ge","family":"Jin","sequence":"first","affiliation":[{"name":"School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China"},{"name":"Intelligent Vision and Sensing Lab, University of Georgia, GA 30602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanghe","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peiliang","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongjing","family":"Hong","sequence":"additional","affiliation":[{"name":"School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingting","family":"Xu","sequence":"additional","affiliation":[{"name":"Intelligent Vision and Sensing Lab, University of Georgia, GA 30602, USA"},{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoyu","family":"Lu","sequence":"additional","affiliation":[{"name":"Intelligent Vision and Sensing Lab, University of Georgia, GA 30602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Essid, O., Laga, H., and Samir, C. (2018). Automatic detection and classification of manufacturing defects in metal boxes using deep neural networks. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0203192"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"23488","DOI":"10.1109\/ACCESS.2019.2898215","article-title":"Surface defect classification for hot-rolled steel strips by selectively dominant local binary patterns","volume":"7","author":"Luo","year":"2019","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1109\/TIM.2019.2963555","article-title":"Automated visual defect detection for flat steel surface: A survey","volume":"69","author":"Luo","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ahonen, T., Hadid, A., and Pietik\u00e4inen, M. (2004, January 11\u201314). Face recognition with local binary patterns. Proceedings of the 8th European Conference on Computer Vision, Prague, Czech Republic.","DOI":"10.1007\/978-3-540-24670-1_36"},{"key":"ref_5","unstructured":"Lienhart, R., and Maydt, J. (2002, January 22\u201325). An extended set of haar-like features for rapid object detection. Proceedings of the International Conference on Image Processing, Rochester, NY, USA."},{"key":"ref_6","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society 131 Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Aghdam, S.R., Amid, E., and Imani, M.F. (2012, January 18\u201320). A fast method of steel surface defect detection using decision trees applied to LBP based 133 features. Proceedings of the 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA), Singapore.","DOI":"10.1109\/ICIEA.2012.6360951"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1109\/TIM.2012.2218677","article-title":"Automatic defect detection on hot-rolled flat steel products","volume":"62","author":"Ghorai","year":"2012","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_9","unstructured":"Shumin, D., Zhoufeng, L., and Chunlei, L. (2011, January 26\u201328). AdaBoost learning for fabric defect detection based on HOG and SVM. Proceedings of the 2011 International Conference on Multimedia Technology, Hangzhou, China."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"53040","DOI":"10.1109\/ACCESS.2019.2912200","article-title":"Review of deep learning algorithms and architectures","volume":"7","author":"Shrestha","year":"2019","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.optlaseng.2019.05.005","article-title":"A deep-learning-based approach for fast and robust steel surface defects classification","volume":"121","author":"Fu","year":"2019","journal-title":"Opt. Lasers Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"129707","DOI":"10.1016\/j.matlet.2021.129707","article-title":"Automatic recognition of surface defects for hot-rolled steel strip based on deep attention residual convolutional neural network","volume":"293","author":"Huang","year":"2021","journal-title":"Mater. Lett."},{"key":"ref_13","first-page":"76","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-Pap."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.3390\/app8091575","article-title":"Automatic metallic surface defect detection and recognition with convolutional neural networks","volume":"8","author":"Tao","year":"2018","journal-title":"Appl. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1109\/TIM.2019.2915404","article-title":"An end-to-end steel surface defect detection approach via fusing multiple hierarchical features","volume":"69","author":"He","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"108208","DOI":"10.1016\/j.compeleceng.2022.108208","article-title":"Surface defect detection of steel strips based on improved YOLOv4","volume":"102","author":"Li","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_17","first-page":"811","article-title":"Performance evaluation of machine learning and deep learning 153 algorithms in crop classification: Impact of hyper-parameters and training sample size","volume":"34","author":"Kim","year":"2018","journal-title":"Korean J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1111\/1754-9485.13261","article-title":"A review of medical image data augmentation techniques for deep learning applications","volume":"65","author":"Chlap","year":"2021","journal-title":"J. Med. Imaging Radiat. Oncol."},{"key":"ref_20","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_22","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_23","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_25","first-page":"1","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_26","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/j.procs.2018.05.198","article-title":"An analysis of convolutional neural networks for image classification","volume":"132","author":"Sharma","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.F. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_28","unstructured":"Li, F.F., Andreeto, M., Ranzato, M., and Perona, P. (2022, July 23). Caltech 101 (1.0) [Data Set]. CaltechDATA. Available online: https:\/\/doi.org\/10.22002\/D1.20086."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (voc) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vision"},{"key":"ref_30","unstructured":"Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1007\/978-3-642-35289-8_25","article-title":"Stochastic gradient descent tricks","volume":"7700","author":"Bottou","year":"2012","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_32","first-page":"123","article-title":"Classification of surface defects on steel sheet using convolutional neural networks","volume":"51","author":"Zhou","year":"2017","journal-title":"Mater. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"95","DOI":"10.3389\/fnins.2019.00095","article-title":"Going deeper in spiking neural networks: VGG and residual architectures","volume":"13","author":"Sengupta","year":"2019","journal-title":"Front. Neurosci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1016\/j.procir.2018.03.264","article-title":"A new ensemble approach based on deep convolutional neural networks for steel surface defect classification","volume":"72","author":"Chen","year":"2018","journal-title":"Procedia CIRP"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/1953\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:28:58Z","timestamp":1760120938000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/1953"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,9]]},"references-count":34,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23041953"],"URL":"https:\/\/doi.org\/10.3390\/s23041953","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,9]]}}}