{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:26:59Z","timestamp":1771025219744,"version":"3.50.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T00:00:00Z","timestamp":1733788800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T00:00:00Z","timestamp":1733788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Fujian Provincial Department of Science and Technology university joint innovation project","award":["2021H6010"],"award-info":[{"award-number":["2021H6010"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s11554-024-01592-9","type":"journal-article","created":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T01:36:12Z","timestamp":1733794572000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A lightweight deep learning model for real-time rectangle NdFeB surface defect detection with high accuracy on a global scale"],"prefix":"10.1007","volume":"22","author":[{"given":"Lin","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heping","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuixuan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihuang","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,10]]},"reference":[{"issue":"7","key":"1592_CR1","first-page":"65","volume":"36","author":"Q Xiao","year":"2022","unstructured":"Xiao, Q., Wang, J.Q., Jin, L.P.: Research progress of key technology and process of magnetorheological finishing. Mater. Rep. 36(07), 65\u201374 (2022)","journal-title":"Mater. Rep."},{"issue":"9","key":"1592_CR2","first-page":"136","volume":"41","author":"D Li","year":"2020","unstructured":"Li, D., et al.: Study on magnetic field distribution in permanent magnetic needle drive using hybrid magnetic suspension needle. Fangzhi Xuebao\/J. Text. Res. 41(9), 136\u2013142 (2020)","journal-title":"Fangzhi Xuebao\/J. Text. Res."},{"key":"1592_CR3","doi-asserted-by":"crossref","unstructured":"Jun, M., et al.: Effect of inner diameter of hollow cylindrical permanent magnet on levitation force of single domain GdBCO bulk superconductor. ACTA Phys. Sin. 67(7), 259-265 (2018)","DOI":"10.7498\/aps.67.20172418"},{"issue":"6","key":"1592_CR4","first-page":"28","volume":"43","author":"F Guo","year":"2022","unstructured":"Guo, F., et al.: Technology equipment and application of sintered neodymium iron boron strip casting. Non-Ferrous Metallur. Des. Res. 43(6), 28\u201331 (2022)","journal-title":"Non-Ferrous Metallur. Des. Res."},{"issue":"2","key":"1592_CR5","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.eng.2018.11.034","volume":"6","author":"JMD Coey","year":"2020","unstructured":"Coey, J.M.D.: Perspective and prospects for rare earth permanent magnets. Engineering 6(2), 119\u2013131 (2020)","journal-title":"Engineering"},{"key":"1592_CR6","doi-asserted-by":"crossref","unstructured":"Li, K., Liu, R.: Optimization design method for excitation magnetic field in motion induced eddy current magnetic field testing. Acta Phys. Sin. 72(16), 292\u2013303 (2023)","DOI":"10.7498\/aps.72.20230064"},{"key":"1592_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ndteint.2024.103052","volume":"143","author":"A Shi","year":"2024","unstructured":"Shi, A., et al.: Lightweight detector based on knowledge distillation for magnetic particle inspection of forgings. NDT & E Int. 143, 103052 (2024)","journal-title":"NDT & E Int."},{"key":"1592_CR8","doi-asserted-by":"crossref","unstructured":"Girshick, R., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014)","DOI":"10.1109\/CVPR.2014.81"},{"issue":"6","key":"1592_CR9","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2016","unstructured":"Ren, S., et al.: Faster R-CNN: Towards Real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1592_CR10","unstructured":"Liu, W., et al.: SSD: Single shot multibox detector. Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I, 14. Springer International Publishing (2016)"},{"key":"1592_CR11","doi-asserted-by":"crossref","unstructured":"Redmon, J., et al.: You only look once: unified, Real-time object detection. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"1592_CR12","doi-asserted-by":"crossref","unstructured":"Carion, N., et al.: End-to-end object detection with transformers. In: European Conference on Computer Vision, Springer (2020)","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"1592_CR13","first-page":"1","volume":"71","author":"Y He","year":"2022","unstructured":"He, Y., et al.: Track defect detection for high-speed maglev trains via deep learning. IEEE Trans. Instrum. Meas. 71, 1\u20138 (2022)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"1","key":"1592_CR14","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1007\/s00371-023-02789-y","volume":"40","author":"F Xin","year":"2024","unstructured":"Xin, F., Zhang, H., Pan, H.: Hybrid dilated multilayer faster RCNN for object detection. Vis. Comput. 40(1), 393\u2013406 (2024)","journal-title":"Vis. Comput."},{"issue":"24","key":"1592_CR15","doi-asserted-by":"publisher","first-page":"9897","DOI":"10.3390\/s22249897","volume":"22","author":"X Lang","year":"2022","unstructured":"Lang, X., et al.: MR-YOLO: an improved YOLOv5 network for detecting magnetic ring surface defects. Sensors 22(24), 9897 (2022)","journal-title":"Sensors"},{"key":"1592_CR16","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"1592_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2023.108304","volume":"214","author":"X Du","year":"2023","unstructured":"Du, X., et al.: DSW-YOLO: a detection method for ground-planted strawberry fruits under different occlusion levels. Comput. Electron. Agric. 214, 108304 (2023)","journal-title":"Comput. Electron. Agric."},{"issue":"1","key":"1592_CR18","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s00371-018-1588-5","volume":"36","author":"Y Huang","year":"2020","unstructured":"Huang, Y., Qiu, C., Yuan, K.: Surface defect saliency of magnetic tile. Vis. Comput. 36(1), 85\u201396 (2020)","journal-title":"Vis. Comput."},{"issue":"9","key":"1592_CR19","doi-asserted-by":"publisher","first-page":"3467","DOI":"10.3390\/s22093467","volume":"22","author":"Z Guo","year":"2022","unstructured":"Guo, Z., et al.: Msft-yolo: improved yolov5 based on transformer for detecting defects of steel surface. Sensors 22(9), 3467 (2022)","journal-title":"Sensors"},{"key":"1592_CR20","unstructured":"Howard, A. G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications (2017). arXiv preprint arXiv:1704.04861"},{"key":"1592_CR21","doi-asserted-by":"crossref","unstructured":"Yang, Z., et al.: Reppoints: point set representation for object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00975"},{"key":"1592_CR22","unstructured":"Wang, C., Liu, H.: YOLOv8-VSC: a lightweight strip surface defect detection algorithm. J. Front. Comput. Sci. Technol. 18(1), 151\u2013160 (2024)"},{"key":"1592_CR23","doi-asserted-by":"crossref","unstructured":"Huangfu, Z., Li, S., Yan, L.: Ghost-YOLOv8: an attention-guided enhanced small target detection algorithm for floating litter on water surfaces. Computers, Materials & Continua. 80(3). 3713\u20133731 (2024)","DOI":"10.32604\/cmc.2024.054188"},{"issue":"6","key":"1592_CR24","doi-asserted-by":"publisher","first-page":"12013","DOI":"10.3233\/JIFS-232874","volume":"45","author":"G Zhang","year":"2023","unstructured":"Zhang, G., et al.: A global lightweight deep learning model for express package detection. J. Intell. Fuzzy Syst. 45(6), 12013\u201312025 (2023)","journal-title":"J. Intell. Fuzzy Syst."},{"key":"1592_CR25","unstructured":"Fang, M., et al.: DHER: hindsight experience replay for dynamic goals. In: International Conference on Learning Representations (2018)"},{"key":"1592_CR26","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: YOLOv8-QSD: an improved small object detection algorithm for autonomous vehicles based on YOLOv8. IEEE Trans. Instrum. Meas. 73, 2513916 (2024)","DOI":"10.1109\/TIM.2024.3379090"},{"key":"1592_CR27","doi-asserted-by":"publisher","unstructured":"Wang, R., Zhao, J., Liu, X., et al.: AdvYOLO: Advanced YOLOv8 application for bone pathology localization and classification in wrist X-ray images. Research Square (2024). https:\/\/doi.org\/10.21203\/rs.3.rs-4051336\/v1","DOI":"10.21203\/rs.3.rs-4051336\/v1"},{"issue":"20","key":"1592_CR28","doi-asserted-by":"publisher","first-page":"8374","DOI":"10.3390\/s23208374","volume":"23","author":"S Saydirasulovich","year":"2023","unstructured":"Saydirasulovich, S., Saydirasulov, N., et al.: An improved wildfire smoke detection based on YOLOv8 and UAV images. Sensors 23(20), 8374 (2023)","journal-title":"Sensors"},{"issue":"1","key":"1592_CR29","doi-asserted-by":"publisher","first-page":"302","DOI":"10.3390\/agriengineering6010018","volume":"6","author":"DC Trinh","year":"2024","unstructured":"Trinh, D.C., et al.: Alpha-EIOU-YOLOv8: an improved algorithm for rice leaf disease detection. AgriEngineering 6(1), 302\u2013317 (2024)","journal-title":"AgriEngineering"},{"issue":"16","key":"1592_CR30","doi-asserted-by":"publisher","first-page":"7190","DOI":"10.3390\/s23167190","volume":"23","author":"G Wang","year":"2023","unstructured":"Wang, G., et al.: UAV-YOLOv8: a small-object-detection model based on improved YOLOv8 for UAV aerial photography scenarios. Sensors 23(16), 7190 (2023)","journal-title":"Sensors"},{"key":"1592_CR31","doi-asserted-by":"crossref","unstructured":"Jin, X., Li, C.: Leather surface defect detection based on improved YOLOv8. In: Proceedings of the 2023 4th International Conference on Computer Science and Management Technology (2023)","DOI":"10.1145\/3644523.3644668"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01592-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-024-01592-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01592-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T17:18:22Z","timestamp":1738603102000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-024-01592-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,10]]},"references-count":31,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["1592"],"URL":"https:\/\/doi.org\/10.1007\/s11554-024-01592-9","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,10]]},"assertion":[{"value":"23 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2024","order":3,"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 that there is no Conflict of interest regarding the publication of this research paper entitled \"A Lightweight Deep Learning Model for Real-Time Rectangle NdFeB Surface Defect Detection with High Accuracy on a Global Scale\". We certify that no financial support or relationships with any organizations or individuals that could potentially bias the interpretation of the research findings or affect the objectivity and integrity of this work were involved. The authors further declare that they have no financial interests, commercial affiliations or patent holdings that are directly or indirectly related to the subject matter of this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This research was conducted in an unbiased and ethical manner, adhering to the highest standards of scientific integrity and research ethics.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Written consent for the publication of identifiable data, images, or other personal or confidential information that may compromise the privacy or anonymity of the participants was obtained from all participants and\/or their legal guardians.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"17"}}