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oai:ebyu-rdlopen.rawdatalibrary.net:ebyu/105572026-10-18T21:13:10Zopenairedrivercom_ebyu_10000col_ebyu_10001

Mean shift based prototypical network for steel surface anomaly recognitionTastimur, Canansteelarticlecontrolled studyfew shot learninghumanproduct qualitySteel production plays a crucial role in the global economy, and surface defects in hot-rolled steel sheets significantly affect product quality and consumer acceptance. This paper introduces Mean Shift based Prototypical network (MSPro-Net), a novel and effective Prototypical network based on mean drift for classifying surface defects early in the manufacturing process. Key contributions of this study include the introduction of MSPro-Net’s adaptive prototype computation, which uses the mean-shift method to improve defect detection. Unlike the classical Prototypical network (CL-ProNet), MSPro-Net produces a prototype that better represents all examples within a class, leading to significantly higher accuracy. Additionally, MSPro-Net is highly effective in few-shot learning scenarios, achieving excellent results with limited training data. Extensive experimental evaluations demonstrate that MSPro-Net consistently outperforms CL-ProNet in multiple N-way K-shot scenarios. For example, on the 6-way 25-shot NEU dataset, MSPro-Net achieves 98.67% accuracy com-pared to 50.67% for the classical network. Similar improvements are observed on the 7-way 25-shot XSDD and 10-way 25-shot GC10-Det datasets, with MSPro-Net achieving 96.00% and 90.00% accuracy, respectively, far exceeding the classical network’s performance. These results highlight the superior performance of MSPro-Net across both small and large datasets. © The Author(s) 2025.Nature Research2025Articlehttps://rdlopen.rawdatalibrary.net/r/ebyu/10557https://doi.org/10.1038/s41598-025-31529-62045-2322enScientific Reportsinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/10557