{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T08:02:27Z","timestamp":1784361747588,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819234288","type":"print"},{"value":"9789819234295","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T00:00:00Z","timestamp":1784419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T00:00:00Z","timestamp":1784419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-981-92-3429-5_49","type":"book-chapter","created":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T07:05:43Z","timestamp":1784358343000},"page":"597-608","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LGCG-Net: Sequential Local-Global Refinement for Imbalanced Industrial Defect Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7392-1498","authenticated-orcid":false,"given":"Hongyi","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaxing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,19]]},"reference":[{"key":"49_CR1","doi-asserted-by":"publisher","DOI":"10.1115\/1.4049535","volume":"21","author":"PM Bhatt","year":"2021","unstructured":"Bhatt, P.M., et al.: Image-based surface defect detection using deep learning: a review. J. Comput. Inf. Sci. Eng. 21, 040801 (2021)","journal-title":"J. Comput. Inf. Sci. Eng."},{"issue":"10","key":"49_CR2","doi-asserted-by":"publisher","first-page":"193","DOI":"10.3390\/jimaging9100193","volume":"9","author":"E Cumbajin","year":"2023","unstructured":"Cumbajin, E., et al.: A systematic review on deep learning with CNNs applied to surface defect detection. J. Imaging. 9(10), 193 (2023)","journal-title":"J. Imaging"},{"issue":"12","key":"49_CR3","doi-asserted-by":"publisher","first-page":"4787","DOI":"10.1109\/TIM.2019.2899478","volume":"68","author":"J Sun","year":"2019","unstructured":"Sun, J., Wang, P., Luo, Y.-K., Li, W.: Surface defects detection based on adaptive multiscale image collection and convolutional neural networks. IEEE Trans. Instrum. Meas. 68(12), 4787\u20134797 (2019)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"49_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.mtcomm.2021.103105","volume":"30","author":"C Hu","year":"2022","unstructured":"Hu, C., Liao, H., Zhou, T., Zhu, A., Xu, C.: Online recognition of magnetic tile defects based on UPM-DenseNet. Mater. Today Commun. 30, 103105 (2022)","journal-title":"Mater. Today Commun."},{"key":"49_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.111631","volume":"159","author":"J Li","year":"2024","unstructured":"Li, J., Wang, K., He, M., Ke, L., Wang, H.: Attention-based convolution neural network for magnetic tile surface defect classification and detection. Appl. Soft Comput. 159, 111631 (2024)","journal-title":"Appl. Soft Comput."},{"key":"49_CR6","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1007\/s00170-024-13341-0","volume":"132","author":"A Bouguettaya","year":"2024","unstructured":"Bouguettaya, A., Zarzour, H.: CNN-based hot-rolled steel strip surface defects classification: a comparative study between different pre-trained CNN models. Int. J. Adv. Manuf. Technol. 132, 399\u2013419 (2024)","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"49_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"49_CR8","doi-asserted-by":"publisher","unstructured":"Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261\u20132269 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"49_CR9","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., et al.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"49_CR10","unstructured":"Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. In: Proceedings of the 36th International Conference on Machine Learning, pp. 6105\u20136114 (2019)"},{"key":"49_CR11","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"49_CR12","doi-asserted-by":"publisher","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11531\u201311539 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01155","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"49_CR13","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision, pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"49_CR14","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13713\u201313722 (2021)","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"49_CR15","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803 (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"49_CR16","doi-asserted-by":"crossref","unstructured":"Cao, Y., Xu, J., Lin, S., Wei, F., Hu, H.: GCNet: non-local networks meet squeeze-excitation networks and beyond. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 1971\u20131980 (2019)","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"49_CR17","unstructured":"Li, X., Hu, X., Yang, J.: Spatial group-wise enhance: Improving semantic feature learning in convolutional networks. arXiv preprint https:\/\/arxiv.org\/abs\/1905.09646 (2019)"},{"key":"49_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2021.103459","volume":"129","author":"J Bo\u017ei\u010d","year":"2021","unstructured":"Bo\u017ei\u010d, J., Tabernik, D., Sko\u010daj, D.: Mixed supervision for surface-defect detection: from weakly to fully supervised learning. Comput. Ind. 129, 103459 (2021)","journal-title":"Comput. Ind."},{"key":"49_CR19","doi-asserted-by":"publisher","unstructured":"Huang, Y., Qiu, C., Guo, Y., Wang, X., Yuan, K.: Surface defect saliency of magnetic tile. In: 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), pp. 612\u2013617 (2018). https:\/\/doi.org\/10.1109\/COASE.2018.8560423","DOI":"10.1109\/COASE.2018.8560423"},{"key":"49_CR20","first-page":"1","volume":"70","author":"Y Bao","year":"2021","unstructured":"Bao, Y., et al.: Triplet-graph reasoning network for few-shot metal generic surface defect segmentation. IEEE Trans. Instrum. Meas. 70, 1\u201311 (2021)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"49_CR21","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1016\/j.apsusc.2013.09.002","volume":"285","author":"K Song","year":"2013","unstructured":"Song, K., Yan, Y.: A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Appl. Surf. Sci. 285, 858\u2013864 (2013)","journal-title":"Appl. Surf. Sci."},{"key":"49_CR22","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1109\/TIM.2019.2915404","volume":"69","author":"Y He","year":"2020","unstructured":"He, Y., Song, K., Meng, Q., Yan, Y.: An end-to-end steel surface defect detection approach via fusing multiple hierarchical features. IEEE Trans. Instrum. Meas. 69, 1493\u20131504 (2020)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"49_CR23","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference on Learning Representations (ICLR 2015), pp. 1\u201314 (2015)"},{"key":"49_CR24","doi-asserted-by":"publisher","unstructured":"Zeng, X., Lu, Y., Wang, H.: CoGR-MoE: concept-guided expert routing with consistent selection and flexible reasoning for visual question answering. In: Findings of the Association for Computational Linguistics: ACL 2026, pp. 6333\u20136350 (2026). https:\/\/doi.org\/10.18653\/v1\/2026.findings-acl.315","DOI":"10.18653\/v1\/2026.findings-acl.315"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3429-5_49","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T07:05:45Z","timestamp":1784358345000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3429-5_49"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,19]]},"ISBN":["9789819234288","9789819234295"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3429-5_49","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,19]]},"assertion":[{"value":"19 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toronto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2026\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}