{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T11:49:13Z","timestamp":1775476153083,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"8-9","license":[{"start":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T00:00:00Z","timestamp":1716336000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T00:00:00Z","timestamp":1716336000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["32201666"],"award-info":[{"award-number":["32201666"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"AIMS Commissioned Project","award":["2022340101001837"],"award-info":[{"award-number":["2022340101001837"]}]},{"name":"Anhui University Power Quality Engineering Research Center\uff0cMinistry of Education","award":["KFKT202304"],"award-info":[{"award-number":["KFKT202304"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s11760-024-03270-6","type":"journal-article","created":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T15:02:06Z","timestamp":1716390126000},"page":"5775-5786","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Surface defect identification method for hot-rolled steel plates based on random data balancing and lightweight convolutional neural network"],"prefix":"10.1007","volume":"18","author":[{"given":"Weihui","family":"Zeng","sequence":"first","affiliation":[]},{"given":"Junyan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Zhimin","family":"Zhong","sequence":"additional","affiliation":[]},{"given":"Gensheng","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Wenxia","family":"Bao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,22]]},"reference":[{"key":"3270_CR1","first-page":"737","volume":"52","author":"A Aldunin","year":"2017","unstructured":"Aldunin, A.: Development of method for calculation of structure parameters of hot-rolled steel strip for sheet stamping. J. Chem. Technol. Metall 52, 737\u2013740 (2017)","journal-title":"J. Chem. Technol. Metall"},{"issue":"1","key":"3270_CR2","doi-asserted-by":"publisher","first-page":"17","DOI":"10.3390\/coatings13010017","volume":"13","author":"X Wen","year":"2022","unstructured":"Wen, X., et al.: Steel surface defect recognition: a survey. Coatings 13(1), 17 (2022)","journal-title":"Coatings"},{"issue":"1","key":"3270_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13640-017-0197-y","volume":"2017","author":"M Xiao","year":"2017","unstructured":"Xiao, M., et al.: An evolutionary classifier for steel surface defects with small sample set. EURASIP J. Image Video Process. 2017(1), 1\u201313 (2017)","journal-title":"EURASIP J. Image Video Process."},{"key":"3270_CR4","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.chemolab.2017.11.018","volume":"172","author":"R Gong","year":"2018","unstructured":"Gong, R., Chengdong, Wu., Chu, M.: Steel surface defect classification using multiple hyper-spheres support vector machine with additional information. Chemom. Intell. Lab. Syst. 172, 109\u2013117 (2018)","journal-title":"Chemom. Intell. Lab. Syst."},{"issue":"20","key":"3270_CR5","doi-asserted-by":"publisher","first-page":"4222","DOI":"10.3390\/app9204222","volume":"9","author":"Y Liu","year":"2019","unstructured":"Liu, Y., Ke, Xu., Jinwu, Xu.: An improved MB-LBP defect recognition approach for the surface of steel plates. Appl. Sci. 9(20), 4222 (2019)","journal-title":"Appl. Sci."},{"issue":"19","key":"3270_CR6","doi-asserted-by":"publisher","first-page":"2359","DOI":"10.3390\/math9192359","volume":"9","author":"X Feng","year":"2021","unstructured":"Feng, X., Gao, X., Luo, L.: A ResNet50-based method for classifying surface defects in hot-rolled strip steel. Mathematics 9(19), 2359 (2021)","journal-title":"Mathematics"},{"key":"3270_CR7","doi-asserted-by":"crossref","unstructured":"Feng, X., Gao, X., and Luo, L.: A method for surface detect classification of hot rolled strip steel based on Xception. In: 2021 33rd Chinese Control and Decision Conference (CCDC). IEEE (2021)","DOI":"10.1109\/CCDC52312.2021.9601541"},{"issue":"2","key":"3270_CR8","doi-asserted-by":"publisher","first-page":"311","DOI":"10.3390\/met12020311","volume":"12","author":"Z Hao","year":"2022","unstructured":"Hao, Z., et al.: Strip steel surface defects classification based on generative adversarial network and attention mechanism. Metals 12(2), 311 (2022)","journal-title":"Metals"},{"key":"3270_CR9","doi-asserted-by":"crossref","unstructured":"Wang, S. et al.: Training deep neural networks on imbalanced data sets. In: 2016 international joint conference on neural networks (IJCNN). IEEE (2016)","DOI":"10.1109\/IJCNN.2016.7727770"},{"issue":"2","key":"3270_CR10","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1007\/s11119-022-09959-3","volume":"24","author":"Q Feng","year":"2023","unstructured":"Feng, Q., et al.: Online recognition of peanut leaf diseases based on the data balance algorithm and deep transfer learning. Precis. Agric. 24(2), 560\u2013586 (2023)","journal-title":"Precis. Agric."},{"key":"3270_CR11","doi-asserted-by":"crossref","unstructured":"Ding, X. et al.: Scaling up your kernels to 31x31: revisiting large kernel design in cnns. In:\u00a0Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (2022)","DOI":"10.1109\/CVPR52688.2022.01166"},{"key":"3270_CR12","unstructured":"Liu, S. et al.: More convnets in the 2020s: Scaling up kernels beyond 51x51 using sparsity. arXiv preprint arXiv:2207.03620\u00a0(2022)"},{"key":"3270_CR13","unstructured":"Howard, A.G. et al.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861\u00a0(2017)"},{"key":"3270_CR14","doi-asserted-by":"crossref","unstructured":"Sandler, M. et al.: Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"3270_CR15","doi-asserted-by":"crossref","unstructured":"Howard, A. et al.: Searching for mobilenetv3. In: Proceedings of the IEEE\/CVF international conference on computer vision (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"key":"3270_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, X. et al.: Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition (2018)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"3270_CR17","doi-asserted-by":"crossref","unstructured":"Ma, N. et al.: Shufflenet v2: Practical guidelines for efficient cnn architecture design. In: Proceedings of the European conference on computer vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"3270_CR18","doi-asserted-by":"crossref","unstructured":"Han, K. et al.: Ghostnet: More features from cheap operations. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (2020)","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"3270_CR19","first-page":"9969","volume":"35","author":"Y Tang","year":"2022","unstructured":"Tang, Y., et al.: GhostNetv2: enhance cheap operation with long-range attention. Adv. Neural Inform. Process. Syst. 35, 9969\u20139982 (2022)","journal-title":"Adv. Neural Inform. Process. Syst."},{"issue":"4","key":"3270_CR20","doi-asserted-by":"publisher","first-page":"706","DOI":"10.3390\/sym13040706","volume":"13","author":"X Feng","year":"2021","unstructured":"Feng, X., Gao, X., Luo, L.: X-SDD: a new benchmark for hot rolled steel strip surface defects detection. Symmetry 13(4), 706 (2021)","journal-title":"Symmetry"},{"key":"3270_CR21","unstructured":"Luo, W. et al.: Understanding the effective receptive field in deep convolutional neural networks.\u00a0Adv. Neural Inform. Process. Syst.\u00a029 (2016)"},{"key":"3270_CR22","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"3270_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, Q.-L., and Yang, Y.-B.: Sa-net: Shuffle attention for deep convolutional neural networks. In: ICASSP 2021\u20132021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (2021)","DOI":"10.1109\/ICASSP39728.2021.9414568"},{"key":"3270_CR24","doi-asserted-by":"crossref","unstructured":"He, K. et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"3270_CR25","doi-asserted-by":"crossref","unstructured":"Ding, X. et al.: Repvgg: making vgg-style convnets great again. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (2021)","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"3270_CR26","doi-asserted-by":"crossref","unstructured":"Liu, Z. et al.: A convnet for the 2020s. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"3270_CR27","doi-asserted-by":"crossref","unstructured":"He, K. et al.: Identity mappings in deep residual networks. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part IV 14. Springer International Publishing (2016)","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"3270_CR28","doi-asserted-by":"crossref","unstructured":"He, T. et al.: Bag of tricks for image classification with convolutional neural networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (2019)","DOI":"10.1109\/CVPR.2019.00065"},{"key":"3270_CR29","unstructured":"Wightman, R., Touvron, H., and J\u00e9gou, H.: Resnet strikes back: an improved training procedure in timm. arXiv 2021.\u00a0arXiv preprint arXiv:2110.00476"},{"key":"3270_CR30","doi-asserted-by":"crossref","unstructured":"Cubuk, E. D. et al.: Randaugment: Practical automated data augmentation with a reduced search space. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops (2020)","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"3270_CR31","doi-asserted-by":"crossref","unstructured":"Yun, S. et al.: Cutmix: Regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF international conference on computer vision (2019)","DOI":"10.1109\/ICCV.2019.00612"},{"key":"3270_CR32","unstructured":"Zhang, H. et al.: mixup: beyond empirical risk minimization.\u00a0arXiv preprint arXiv:1710.09412\u00a0(2017)"},{"key":"3270_CR33","doi-asserted-by":"crossref","unstructured":"Szegedy, C. et al.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the AAAI conference on artificial intelligence. Vol. 31. No. 1. (2017)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"3270_CR34","unstructured":"Loshchilov, I. and Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts.\u00a0arXiv preprint arXiv:1608.03983\u00a0(2016)"},{"key":"3270_CR35","unstructured":"Goyal, P. et al.: Accurate, large minibatch sgd: training imagenet in 1 hour.\u00a0arXiv preprint arXiv:1706.02677\u00a0(2017)"},{"key":"3270_CR36","unstructured":"Tan, M., and Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: International conference on machine learning. PMLR (2019)"},{"key":"3270_CR37","unstructured":"Micikevicius, P. et al.: Mixed precision training.\u00a0arXiv preprint arXiv:1710.03740\u00a0(2017)"},{"key":"3270_CR38","doi-asserted-by":"crossref","unstructured":"Tishby, N. and Zaslavsky, N.: Deep learning and the information bottleneck principle. In: 2015 IEEE information theory workshop (itw). IEEE (2015)","DOI":"10.1109\/ITW.2015.7133169"},{"issue":"4","key":"3270_CR39","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1109\/TIM.2019.2915404","volume":"69","author":"Y He","year":"2019","unstructured":"He, Y., et al.: An end-to-end steel surface defect detection approach via fusing multiple hierarchical features. IEEE Trans. Instrum. Measur. 69(4), 1493\u20131504 (2019)","journal-title":"IEEE Trans. Instrum. Measur."},{"key":"3270_CR40","unstructured":"Jocher, G. et al.: ultralytics\/yolov5: v7. 0-yolov5 sota realtime instance segmentation.\u00a0Zenodo (2022)"},{"key":"3270_CR41","doi-asserted-by":"crossref","unstructured":"Selvaraju, R. R., et al.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"3270_CR42","unstructured":"Dosovitskiy, A. et al.: An image is worth 16x16 words: transformers for image recognition at scale.\u00a0arXiv preprint arXiv:2010.11929\u00a0(2020)"},{"key":"3270_CR43","doi-asserted-by":"crossref","unstructured":"Liu, Z. et al.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"3270_CR44","unstructured":"Krizhevsky, A. and Hinton, G.: Learning multiple layers of features from tiny images 7 (2009)"},{"key":"3270_CR45","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., et al.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vision 88, 303\u2013338 (2010)","journal-title":"Int. J. Comput. Vision"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03270-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-024-03270-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03270-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T19:04:29Z","timestamp":1722279869000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-024-03270-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,22]]},"references-count":45,"journal-issue":{"issue":"8-9","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["3270"],"URL":"https:\/\/doi.org\/10.1007\/s11760-024-03270-6","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,22]]},"assertion":[{"value":"4 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 May 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}