{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T18:29:30Z","timestamp":1789496970416,"version":"build-2803163510"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T00:00:00Z","timestamp":1722988800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T00:00:00Z","timestamp":1722988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No.62103143"],"award-info":[{"award-number":["No.62103143"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No.62103143"],"award-info":[{"award-number":["No.62103143"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004735","name":"Hunan Provincial Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No.2021JJ40214"],"award-info":[{"award-number":["No.2021JJ40214"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004735","name":"Hunan Provincial Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No.2021JJ40214"],"award-info":[{"award-number":["No.2021JJ40214"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Scientific Research Fund of Hunan Provincial Education Department","award":["No.22B0471"],"award-info":[{"award-number":["No.22B0471"]}]},{"name":"the Scientific Research Fund of Hunan Provincial Education Department","award":["No.22B0471"],"award-info":[{"award-number":["No.22B0471"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s00371-024-03583-0","type":"journal-article","created":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T08:03:11Z","timestamp":1723017791000},"page":"2171-2185","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Efficient minor defects detection on steel surface via res-attention and position encoding"],"prefix":"10.1007","volume":"41","author":[{"given":"Chuang","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingqin","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,7]]},"reference":[{"issue":"2","key":"3583_CR1","doi-asserted-by":"publisher","first-page":"751","DOI":"10.1109\/TITS.2012.2236555","volume":"14","author":"E Resendiz","year":"2013","unstructured":"Resendiz, E., Hart, J.M., Ahuja, N.: Automated visual inspection of railroad tracks. IEEE Trans. Intell. Transp. Syst. 14(2), 751\u2013760 (2013)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"3583_CR2","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."},{"issue":"3","key":"3583_CR3","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1109\/TIM.2012.2218677","volume":"62","author":"S Ghorai","year":"2012","unstructured":"Ghorai, S., Mukherjee, A., Gangadaran, M., Dutta, P.K.: Automatic defect detection on hot-rolled flat steel products. IEEE Trans. Instrum. Meas. 62(3), 612\u2013621 (2012)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"3583_CR4","unstructured":"Zhou, J., Huang, S., Wang, M., Qiu, Y.: Performance evaluation of hybrid GA-SVM and GWO-SVM models to predict earthquake-induced liquefaction potential of soil: a multi-dataset investigation. Eng. Comput., pp. 1\u201319 (2022)"},{"key":"3583_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.optlaseng.2019.106000","volume":"128","author":"G Song","year":"2020","unstructured":"Song, G., Song, K., Yan, Y.: Saliency detection for strip steel surface defects using multiple constraints and improved texture features. Opt. Lasers Eng. 128, 106000 (2020)","journal-title":"Opt. Lasers Eng."},{"key":"3583_CR6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2020.2991573","volume":"70","author":"X Cheng","year":"2020","unstructured":"Cheng, X., Jianbo, Yu.: Retinanet with difference channel attention and adaptively spatial feature fusion for steel surface defect detection. IEEE Trans. Instrum. Meas. 70, 1\u201311 (2020)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"1","key":"3583_CR7","doi-asserted-by":"publisher","first-page":"22136","DOI":"10.1038\/s41598-023-49359-9","volume":"13","author":"Z Chen","year":"2023","unstructured":"Chen, Z., Feng, X., Liu, L., Jia, Z.: Surface defect detection of industrial components based on vision. Sci. Rep. 13(1), 22136 (2023)","journal-title":"Sci. Rep."},{"key":"3583_CR8","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":"3583_CR9","doi-asserted-by":"crossref","unstructured":"Loizillon, S., Bottani, S., Maire, A., Str\u00f6er, S., Dormont, D., Colliot, O., Burgos, N.: Alzheimer\u2019s Disease\u00a0Neuroimaging Initiative, APPRIMAGE\u00a0Study Group, et\u00a0al. Automatic motion artefact detection in brain t1-weighted magnetic resonance images from a clinical data warehouse using synthetic data, vol.\u00a093, p. 103073. Elsevier (2024)","DOI":"10.1016\/j.media.2023.103073"},{"key":"3583_CR10","doi-asserted-by":"crossref","unstructured":"Lee, W., Choi, Y.: Facile preparation of robust anti-wetting membrane by simple two-step feooh and fluorosilane membrane modification, vol. 442, p. 136112. Elsevier (2022)","DOI":"10.1016\/j.cej.2022.136112"},{"key":"3583_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2021.103551","volume":"134","author":"M Chen","year":"2022","unstructured":"Chen, M., Lingjie, Yu., Zhi, C., Sun, R., Zhu, S., Gao, Z., Ke, Z., Zhu, M., Zhang, Y.: Improved faster r-cnn for fabric defect detection based on gabor filter with genetic algorithm optimization. Comput. Ind. 134, 103551 (2022)","journal-title":"Comput. Ind."},{"key":"3583_CR12","doi-asserted-by":"crossref","unstructured":"Ma, J., Wang, Y., Shi, C., Lu, C.: Fast surface defect detection using improved gabor filters. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 1508\u20131512. IEEE (2018)","DOI":"10.1109\/ICIP.2018.8451351"},{"key":"3583_CR13","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.jvcir.2015.04.005","volume":"37","author":"M Liu","year":"2016","unstructured":"Liu, M., Liu, Y., Huijun, H., Nie, L.: Genetic algorithm and mathematical morphology based binarization method for strip steel defect image with non-uniform illumination. J. Vis. Commun. Image Represent. 37, 70\u201377 (2016)","journal-title":"J. Vis. Commun. Image Represent."},{"issue":"6","key":"3583_CR14","doi-asserted-by":"publisher","first-page":"7251","DOI":"10.1016\/j.eswa.2010.12.026","volume":"38","author":"K Agarwal","year":"2011","unstructured":"Agarwal, K., Shivpuri, R., Zhu, Y., Chang, T.-S., Huang, H.: Process knowledge based multi-class support vector classification (pk-msvm) approach for surface defects in hot rolling. Expert Syst. Appl. 38(6), 7251\u20137262 (2011)","journal-title":"Expert Syst. Appl."},{"issue":"17","key":"3583_CR15","doi-asserted-by":"publisher","first-page":"4884","DOI":"10.1364\/AO.57.004884","volume":"57","author":"C-W You","year":"2018","unstructured":"You, C.-W., Chengchangfeng, L., Wang, T.-Y., Qian, S.-R., Yang, Z.-G., Wang, K.-J., Liu, J.-S., Wang, S.-L.: Method for defect contour extraction in terahertz non-destructive testing conducted with a raster-scan thz imaging system. Appl. Opt. 57(17), 4884\u20134889 (2018)","journal-title":"Appl. Opt."},{"key":"3583_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109558","volume":"140","author":"X Hou","year":"2023","unstructured":"Hou, X., Liu, M., Zhang, S., Wei, P., Chen, B.: Canet: contextual information and spatial attention based network for detecting small defects in manufacturing industry. Pattern Recognit. 140, 109558 (2023)","journal-title":"Pattern Recognit."},{"key":"3583_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105628","volume":"117","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., Wang, W., Li, Z., Shu, S., Lang, X., Zhang, T., Dong, J.: Development of a cross-scale weighted feature fusion network for hot-rolled steel surface defect detection. Eng. Appl. Artif. Intell. 117, 105628 (2023)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"3583_CR18","first-page":"1","volume":"71","author":"C-C Yeung","year":"2022","unstructured":"Yeung, C.-C., Lam, K.-M.: Efficient fused-attention model for steel surface defect detection. IEEE Trans. Instrum. Meas. 71, 1\u201311 (2022)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"4","key":"3583_CR19","doi-asserted-by":"publisher","DOI":"10.1115\/1.4063860","volume":"24","author":"Y Wang","year":"2024","unstructured":"Wang, Y., Wang, X., Hao, R., Bingyu, L., Huang, B.: Metal surface defect detection method based on improved cascade r-cnn. J. Comput. Inf. Sci. Eng. 24(4), 041002 (2024)","journal-title":"J. Comput. Inf. Sci. Eng."},{"key":"3583_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.110211","volume":"187","author":"R Tian","year":"2022","unstructured":"Tian, R., Jia, M.: Dcc-centernet: a rapid detection method for steel surface defects. Measurement 187, 110211 (2022)","journal-title":"Measurement"},{"issue":"5","key":"3583_CR21","doi-asserted-by":"publisher","first-page":"2798","DOI":"10.1109\/TII.2018.2887145","volume":"15","author":"H Wang","year":"2018","unstructured":"Wang, H., Zhang, J., Tian, Y., Chen, H., Sun, H., Liu, K.: A simple guidance template-based defect detection method for strip steel surfaces. IEEE Trans. Ind. Inf. 15(5), 2798\u20132809 (2018)","journal-title":"IEEE Trans. Ind. Inf."},{"key":"3583_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2022.101566","volume":"52","author":"X Rongge","year":"2022","unstructured":"Rongge, X., Hao, R., Huang, B.: Efficient surface defect detection using self-supervised learning strategy and segmentation network. Adv. Eng. Inform. 52, 101566 (2022)","journal-title":"Adv. Eng. Inform."},{"key":"3583_CR23","doi-asserted-by":"publisher","first-page":"1833","DOI":"10.1007\/s10845-020-01670-2","volume":"32","author":"R Hao","year":"2021","unstructured":"Hao, R., Bingyu, L., Cheng, Y., Li, X., Huang, B.: A steel surface defect inspection approach towards smart industrial monitoring. J. Intell. Manuf. 32, 1833\u20131843 (2021)","journal-title":"J. Intell. Manuf."},{"key":"3583_CR24","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J.Y., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: a metric and a loss for bounding box regression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 658\u2013666 (2019)","DOI":"10.1109\/CVPR.2019.00075"},{"key":"3583_CR25","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.neucom.2022.07.042","volume":"506","author":"Y-F Zhang","year":"2022","unstructured":"Zhang, Y.-F., Ren, W., Zhang, Z., Jia, Z., Wang, L., Tan, T.: Focal and efficient iou loss for accurate bounding box regression. Neurocomputing 506, 146\u2013157 (2022)","journal-title":"Neurocomputing"},{"key":"3583_CR26","unstructured":"Ross, T.-Y.L.P.G., Doll\u00e1r, G.K.H.P.: Focal loss for dense object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2980\u20132988 (2017)"},{"key":"3583_CR27","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lyu, S., Wang, X., Zhao, Q.: TPH-YOLOv5: improved yolov5 based on transformer prediction head for object detection on drone-captured scenarios. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2778\u20132788 (2021)","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"3583_CR28","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., Sun, J.: Repvgg: making vgg-style convnets great again. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13733\u201313742 (2021)","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"3583_CR29","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 (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"3583_CR30","unstructured":"Liu, R., Lehman, J., Molino, P., Petroski\u00a0Such, F., Frank, E., Sergeev, A., Yosinski, J.: An intriguing failing of convolutional neural networks and the coordconv solution. Adv. Neural Inf. Process. Syst., 31 (2018)"},{"issue":"8","key":"3583_CR31","doi-asserted-by":"publisher","first-page":"8574","DOI":"10.1109\/TCYB.2021.3095305","volume":"52","author":"Z Zheng","year":"2021","unstructured":"Zheng, Z., Wang, P., Ren, D., Liu, W., Ye, R., Qinghua, H., Zuo, W.: Enhancing geometric factors in model learning and inference for object detection and instance segmentation. IEEE Trans. Cybern. 52(8), 8574\u20138586 (2021)","journal-title":"IEEE Trans. Cybern."},{"key":"3583_CR32","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., Ren, D.: Distance-iou loss: faster and better learning for bounding box regression. In: Proceedings of the AAAI Conference on Artificial Intelligence 34, 12993\u201313000 (2020)","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"3583_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, Y.F., Ren, W., Zhang, Z., Jia, Z., Wang, L., Tan, T.: Focal and efficient iou loss for accurate bounding box regression. arXiv preprint arXiv:2101.08158 (2021)","DOI":"10.1016\/j.neucom.2022.07.042"},{"key":"3583_CR34","unstructured":"Siliang, M., Yong, X.: Mpdiou: a loss for efficient and accurate bounding box regression. arXiv preprint arXiv:2307.07662 (2023)"},{"issue":"4","key":"3583_CR35","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1109\/TIM.2019.2915404","volume":"69","author":"Yu He","year":"2019","unstructured":"He, Yu., 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(4), 1493\u20131504 (2019)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"6","key":"3583_CR36","doi-asserted-by":"publisher","first-page":"1562","DOI":"10.3390\/s20061562","volume":"20","author":"X Lv","year":"2020","unstructured":"Lv, X., Duan, F., Jiang, J., Xiao, F., Gan, L.: Deep metallic surface defect detection: the new benchmark and detection network. Sensors 20(6), 1562 (2020)","journal-title":"Sensors"},{"key":"3583_CR37","doi-asserted-by":"crossref","unstructured":"Liu, R., Huang, M., Gao, Z., Cao, Z., Cao, P.: Msc-dnet: an efficient detector with multi-scale context for defect detection on strip steel surface. Measurement 209, 112467 (2023)","DOI":"10.1016\/j.measurement.2023.112467"},{"key":"3583_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2023.113359","volume":"220","author":"S Li","year":"2023","unstructured":"Li, S., Kong, F., Wang, R., Luo, T., Shi, Z.: Efd-yolov4: a steel surface defect detection network with encoder-decoder residual block and feature alignment module. Measurement 220, 113359 (2023)","journal-title":"Measurement"},{"key":"3583_CR39","doi-asserted-by":"crossref","unstructured":"Tian, R., Jia, M.: Dcc-centernet: a rapid detection method for steel surface defects. Measurement 187, 110211 (2022)","DOI":"10.1016\/j.measurement.2021.110211"},{"key":"3583_CR40","first-page":"1","volume":"72","author":"X Xiang","year":"2023","unstructured":"Xiang, X., Wang, Z., Zhang, J., Xia, Y., Chen, P., Wang, B.: Agca: an adaptive graph channel attention module for steel surface defect detection. IEEE Trans. Instrum. Meas. 72, 1\u201312 (2023)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"3583_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110578","volume":"145","author":"H Xuejin","year":"2023","unstructured":"Xuejin, H., Yang, J., Jiang, F., Hussain, A., Dashtipour, K., Gogate, M.: Steel surface defect detection based on self-supervised contrastive representation learning with matching metric. Appl. Soft Comput. 145, 110578 (2023)","journal-title":"Appl. Soft Comput."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03583-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-024-03583-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03583-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T11:31:01Z","timestamp":1741001461000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-024-03583-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,7]]},"references-count":41,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["3583"],"URL":"https:\/\/doi.org\/10.1007\/s00371-024-03583-0","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,7]]},"assertion":[{"value":"13 July 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 August 2024","order":2,"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":"Competing interests"}}]}}