{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T22:13:28Z","timestamp":1784585608107,"version":"3.55.0"},"reference-count":38,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T00:00:00Z","timestamp":1751500800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>With the development of industrialization, the demand for high-performance metal materials has increased, and copper and its alloys have been widely used. The microstructure of these materials significantly affects their performance. To address the issues of subjectivity, low efficiency, and limited quantitative capability in traditional metallographic analysis methods, this paper proposes a deep learning-based approach for segmenting the second phase in Cu-Fe alloys. The method is built upon the YOLO11 framework and incorporates a series of structural enhancements tailored to the characteristics of the secondary-phase microstructure, aiming to improve the model\u2019s detection accuracy and segmentation performance. Specifically, the EIEM module enhances the C3K2 structure to improve edge perception; the CSPSA module is optimized into C2CGA to strengthen multi-scale feature representation; and the RepGFPN and DySample techniques are integrated to construct the GDFPN neck network. Experimental results on the Cu-Fe alloy metallographic image dataset demonstrate that YOLO11 outperforms mainstream semantic segmentation models such as U-Net and DeepLabV3+ in terms of mAP (85.5%), inference speed (208 FPS), and model complexity (10.2 GFLOPs). The improved YOLO11 model achieves an mAP of 89.0%, a precision of 84.6%, and a recall of 81.0% on this dataset, showing significant performance improvements while effectively balancing inference speed and model complexity. Additionally, a quantitative analysis software system for secondary phase uniformity based on this model provides strong technical support for automated metallographic image analysis and demonstrates broad application prospects in materials science research and industrial quality control.<\/jats:p>","DOI":"10.3390\/info16070570","type":"journal-article","created":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T09:57:39Z","timestamp":1751536659000},"page":"570","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A YOLO11-Based Method for Segmenting Secondary Phases in Cu-Fe Alloy Microstructures"],"prefix":"10.3390","volume":"16","author":[{"given":"Qingxiu","family":"Jing","sequence":"first","affiliation":[{"name":"School of Metallurgical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiyang","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Metallurgical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhicong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Materials Science and Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Metallurgical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiqi","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Metallurgical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihui","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Metallurgical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,3]]},"reference":[{"key":"ref_1","first-page":"84","article-title":"Development Status and Prospects of Advanced Copper Alloy","volume":"22","author":"Jiang","year":"2020","journal-title":"Strateg. Study Chin. Acad. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Alireza, V.N., Mahya, G., and Kazem, S.B. (2024). Advancements in Additive Manufacturing for Copper-Based Alloys and Composites: A Comprehensive Review. J. Manuf. Mater. Process., 8.","DOI":"10.3390\/jmmp8020054"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"96","DOI":"10.15302\/J-SSCAE-2023.01.006","article-title":"Development strategy for advanced copper-based materials in China","volume":"25","author":"Mi","year":"2023","journal-title":"Eng. Sci. China"},{"key":"ref_4","first-page":"4171","article-title":"Comparison of Microstructure and Mechanical Properties of Several Cathode Copper Materials","volume":"52","author":"Xu","year":"2023","journal-title":"Rare Met. Mater. Eng."},{"key":"ref_5","first-page":"849","article-title":"Preparation and performance of Cu-Fe alloy","volume":"44","author":"Han","year":"2023","journal-title":"Foundry Eng."},{"key":"ref_6","first-page":"265","article-title":"The effect of aging temperature on the precipitation of Cu-rich phase in Fe-Cu-Mn-Ni alloy was studied by field method","volume":"48","author":"Wang","year":"2023","journal-title":"Met. Meat Treat."},{"key":"ref_7","first-page":"403","article-title":"High performance Cu-Fe alloy strip and its short process preparation technology","volume":"46","author":"Sun","year":"2024","journal-title":"J. Shenyang Univ. Technol."},{"key":"ref_8","unstructured":"Wang, X.C., and Zhang, X.Y. (2010). Modern Analytical and Testing Techniques for Materials, National Defense Industry Press. [2nd ed.]."},{"key":"ref_9","unstructured":"Yao, Y.C., Du, Q., Cai, H., and Jin, J.M. (2021). Metallographic Inspection and Analysis, China Machine Press."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1007\/s40843-020-1368-7","article-title":"Applying deep learning in automatic and rapid measurement of lattice spacings in HRTEM images","volume":"63","author":"Zhu","year":"2020","journal-title":"Sci. China Mater."},{"key":"ref_11","first-page":"68","article-title":"Application and Challenges of Deep Learning in Microstructure Image Analysis of Materials","volume":"28","author":"Ban","year":"2020","journal-title":"Mater. Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"126925","DOI":"10.1016\/j.eswa.2025.126925","article-title":"CMAA: Channel-wise multi-scale adaptive attention network for metallographic image semantic segmentation","volume":"276","author":"Sun","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"122146","DOI":"10.1016\/j.eswa.2023.122146","article-title":"Wire melted mark metallographic image recognition and classification based on semantic segmentation","volume":"238","author":"Shi","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_14","first-page":"78","article-title":"Study on the evaluation method of steel microstructure for thermal power units based on deep learning","volume":"42","author":"Zhang","year":"2024","journal-title":"Inn. Mong. Electr. Power Technol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Azimi, S.M., Britz, D., and Engstler, M. (2018). Advanced Steel Microstructural Classification by Deep Learning Methods. Sci. Rep., 8.","DOI":"10.1038\/s41598-018-20037-5"},{"key":"ref_16","first-page":"6","article-title":"Grain boundary segmentation and restoration based on deep learning and digital image processing","volume":"3","author":"Tao","year":"2024","journal-title":"Rail Transit Mater."},{"key":"ref_17","first-page":"154","article-title":"Study on classification and identification of metallographic structure based on ViT","volume":"3","author":"Yang","year":"2022","journal-title":"Electron. Technol. Softw. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xu, Y.F., Zhang, Y.W., and Zhang, M.Z. (2021). Quantitative Analysis of Metallographic Image Using Attention-Aware Deep Neural Networks. Sensors, 21.","DOI":"10.3390\/s21010043"},{"key":"ref_19","first-page":"66","article-title":"Carburized Gear Metallographic Image Segmentation Algorithm Based on Deep-Learning","volume":"42","author":"Dong","year":"2024","journal-title":"Light Ind. Mach."},{"key":"ref_20","first-page":"3697","article-title":"Deep learning-based segmentation method for pure iron grain microstructure images","volume":"52","author":"Bu","year":"2024","journal-title":"Comput. Digit. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"954","DOI":"10.2355\/isijinternational.ISIJINT-2019-568","article-title":"Development of high accuracy segmentation model for microstructure of steel by deep learning","volume":"60","author":"Ajioka","year":"2022","journal-title":"ISIJ Int."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1080\/13621718.2019.1687635","article-title":"Residual neural network-based fully convolutional network for microstructure segmentation","volume":"25","author":"Jang","year":"2022","journal-title":"Sci. Technol. Weld. Join."},{"key":"ref_23","first-page":"1","article-title":"Deep learning-based strengthening and refinement analysis of microstructure characteristics of ultra-low carbon steel","volume":"60","author":"Wang","year":"2024","journal-title":"Steel"},{"key":"ref_24","first-page":"246","article-title":"Residual network-based microstructure image segmentation method for high-temperature alloys","volume":"20","author":"Zhang","year":"2020","journal-title":"Sci. Technol. Eng."},{"key":"ref_25","unstructured":"He, H.Z. (2022). Automation Recognition of Steel Metallographic Structure Based on Deep Learning. [Master\u2019s Thesis, Wuhan University of Engineering]."},{"key":"ref_26","first-page":"120","article-title":"Deep learning and region-aware method for polycrystalline microstructure image segmentation","volume":"25","author":"Ma","year":"2020","journal-title":"Chin. J. Stereol. Image Anal."},{"key":"ref_27","unstructured":"Ye, W.L. (2024). Research on Identification and Segmentation Method of Metallographic Structure of Heat-Resistant Steel Based on Deep Learning. [Master\u2019s Thesis, . Inner Mongolia Agricultural University]."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023, January 17\u201324). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Yeh, I.H., and Mark Liao, H.Y. (2024). Yolov9: Learning what you want to learn using programmable gradient information. European Conference on Computer Vision, Springer Nature.","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"ref_31","unstructured":"Wang, A., Chen, H., and Liu, L. (2024). Yolov10: Real-time end-to-end object detection. arXiv."},{"key":"ref_32","unstructured":"Khanam, R., and Hussain, M. (2024). Yolov11: An overview of the key architectural enhancements. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, X., Peng, H., and Zheng, N. (2023, January 17\u201324). Efficientvit: Memory efficient vision transformer with cascaded group attention. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01386"},{"key":"ref_34","unstructured":"Xu, X., Jiang, Y., and Chen, W. (2022). Damo-yolo: A report on real-time object detection design. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liu, W., Lu, H., and Fu, H. (2023, January 1\u20136). Learning to upsample by learning to sample. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00554"},{"key":"ref_36","first-page":"20","article-title":"Road Surface Crack Detection Based on Improved YOLOv8n","volume":"30","author":"He","year":"2024","journal-title":"Mod. Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_38","unstructured":"Sun, Y.Y., and Wang, S. (2023). Rapid Development and Practical Projects with PySide6\/PyQt6, Electronics Industry Press."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/7\/570\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:04:04Z","timestamp":1760033044000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/7\/570"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,3]]},"references-count":38,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["info16070570"],"URL":"https:\/\/doi.org\/10.3390\/info16070570","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,3]]}}}