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To address these limitations, we propose a novel framework based on the YOLOv11 architecture, termed adaptive hybrid dynamic YOLO (AHD\u2010YOLO). AHD\u2010YOLO introduces three key innovations. Omni\u2010dimensional dynamic fusion (ODFusion) enhances the adaptability and precision of feature extraction. Adaptive in\u2010scale feature interaction (AIFI) captures fine\u2010grained damage features. Adaptive high\u2010level screening feature fusion pyramid network (AHSFPN) emphasizes critical damage regions while maintaining a lightweight design. Experiments conducted on the building damage dataset show that AHD\u2010YOLO achieves 70.5% mAP, 60.2% Recall, and 48.2% mAP@0.5:0.95, representing respective improvements of 2.1%, 1.3%, and 1.7% over YOLOv11s. Moreover, the model also reduces the number of parameters and GFLOPs by 11.0% and 13.0%, respectively. Comparative experiments indicate that AHD\u2010YOLO outperforms current state\u2010of\u2010the\u2010art detection methods. In generalization tests on a structural damage dataset, the model achieves 78.3% detection accuracy, exceeding YOLOv11s by 2.7%. These results confirm that AHD\u2010YOLO effectively balances detection precision and computational efficiency, enabling accurate and real\u2010time identification of multiple damage types in practical building inspection scenarios.<\/jats:p>","DOI":"10.1049\/ipr2.70298","type":"journal-article","created":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T20:49:37Z","timestamp":1770238177000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AHD\u2010YOLO: An Adaptive Hybrid Dynamic Network for Building Damage Detection"],"prefix":"10.1049","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8461-1290","authenticated-orcid":false,"given":"Min","family":"Li","sequence":"first","affiliation":[{"name":"Keimyung Academy at Changchun University  Changchun China"}]},{"given":"Tao","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering Changchun University  Changchun China"}]},{"given":"Yinping","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering Changchun University  Changchun China"}]},{"given":"Peiyong","family":"Ji","sequence":"additional","affiliation":[{"name":"College of Mechanical and Vehicular Engineering Changchun University  Changchun China"}]},{"given":"Jingqi","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Vehicular Engineering Changchun University  Changchun China"}]},{"given":"Wenlong","family":"Lui","sequence":"additional","affiliation":[{"name":"College of Mechanical and Vehicular Engineering Changchun University  Changchun China"}]},{"given":"Xuejian","family":"Ji","sequence":"additional","affiliation":[{"name":"College of Mechanical and Vehicular Engineering Changchun University  Changchun China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3825-983X","authenticated-orcid":false,"given":"Ruiqiang","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Mechanical and Vehicular Engineering Changchun University  Changchun China"}]}],"member":"265","published-online":{"date-parts":[[2026,2,4]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)CO.1943\u20107862.0001500"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0887\u20103828(2006)20:3(213)"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41024\u2010020\u201000084\u20100"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.buildenv.2020.107575"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2015.2428998"},{"key":"e_1_2_10_7_1","doi-asserted-by":"crossref","unstructured":"K.Li D.Wang Z.Hu et\u00a0al. \u201cFd2\u2010net: Frequency\u2010Driven Feature Decomposition Network for Infrared\u2010Visible Object Detection \u201d inProceedings of the AAAI Conference on Artificial Intelligence39 no. 5 (2025):4797\u20134805.","DOI":"10.1609\/aaai.v39i5.32507"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2019.04.021"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/7068349"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3525183"},{"key":"e_1_2_10_12_1","doi-asserted-by":"crossref","unstructured":"R.Girshick J.Donahue T.Darrell andJ.Malik \u201cRich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation \u201d2014 IEEE Conference on Computer Vision and Pattern Recognition(Columbus OH USA 2014) pp.580\u2013587 https:\/\/doi.org\/10.1109\/CVPR.2014.81.","DOI":"10.1109\/CVPR.2014.81"},{"key":"e_1_2_10_13_1","doi-asserted-by":"crossref","unstructured":"R.Girshick \u201cFast R\u2010CNN \u201d InProceedings of the IEEE International Conference on Computer Vision (ICCV)(2015):1440\u20131448.","DOI":"10.1109\/ICCV.2015.169"},{"key":"e_1_2_10_14_1","first-page":"91","article-title":"Faster R\u2010CNN: Towards Real\u2010Time Object Detection with Region Proposal Networks","volume":"28","author":"Ren S.","year":"2015","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_10_15_1","doi-asserted-by":"crossref","unstructured":"K.He G.Gkioxari P.Doll\u00e1r andR.Girshick \u201cMask R\u2010CNN \u201d inProceedings of the IEEE International Conference on Computer Vision(ICCV) (2017):2961\u20132969.","DOI":"10.1109\/ICCV.2017.322"},{"key":"e_1_2_10_16_1","article-title":"STD2: Swin Transformer\u2010Based Defect Detector for Surface Anomaly Detection","volume":"74","author":"Mia M. 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