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Current methodologies, however, face substantial challenges, including detection accuracy limitations, low\u2010resolution image processing difficulties, background noise interference, and target occlusion issues. To address these challenges, we propose FMF\u2010DETR, an innovative small object detection framework featuring frequency\u2010domain feature optimization through three key components: (1) High\u2010Low Frequency Fusion Model (HLFM), (2) Focused Diffusion Feature Pyramid Network (FDFPN), and (3) BiPathNet (BPNet). Specifically, the HLFM module enhances multiscale feature representation by emphasizing high\u2010frequency details while suppressing low\u2010frequency background noise. The FDFPN architecture improves detection performance in complex scenarios through multiscale feature fusion and saliency\u2010aware diffusion. BPNet introduces a dual\u2010path feature extraction mechanism that simultaneously enhances feature discriminability and reduces computational overhead. Through the synergistic integration of these components, the proposed framework enhances both detection accuracy and operational efficiency. Comprehensive evaluations on the VisDrone dataset demonstrate FMF\u2010DETR's superior performance, achieving a 2.2% accuracy improvement while reducing model parameters by 13.03M and computational complexity by 97.2G FLOPs compared to baseline methods. These results validate both the effectiveness and efficiency of our proposed framework.<\/jats:p>","DOI":"10.1002\/cpe.70597","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T03:56:19Z","timestamp":1770004579000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FMF\u2010DETR: A Frequency\u2010Aware Multi\u2010Scale Fusion DETR for Small Object Detection"],"prefix":"10.1002","volume":"38","author":[{"given":"Lingling","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science Zhengzhou University of Aeronautics  Zhengzhou China"},{"name":"Henan Province Multi\u2010Modal Information Perception and Computing Engineering Research Center  Zhengzhou China"},{"name":"Henan Province Aerospace Intelligent Technology and Systems International Joint Laboratory  Zhengzhou China"},{"name":"Collaborative Innovation Center of Aeronautics and Astronautics Electronic Information Technology  Zhengzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Mei","sequence":"additional","affiliation":[{"name":"School of Computer Science Zhengzhou University of Aeronautics  Zhengzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuezhuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science Zhengzhou University of Aeronautics  Zhengzhou China"},{"name":"National Key Laboratory of Air\u2010based Information Perception and Fusion  Luoyang China"},{"name":"Chongqing Research Institute of HIT  Chongqing China"},{"name":"Aerospace Electronic Information Technology Henan Collaborative Innovation Center  Zhengzhou China"},{"name":"Henan Province Artificial Intelligence Technology School\u2010Enterprise Research and Development Center  Zhengzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyan","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Computer Science Zhengzhou University of Aeronautics  Zhengzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zonghao","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science Zhengzhou University of Aeronautics  Zhengzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiqin","family":"Diao","sequence":"additional","affiliation":[{"name":"School of Computer Science Zhengzhou University of Aeronautics  Zhengzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mai","family":"Xu","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center of Aeronautics and Astronautics Electronic Information Technology  Zhengzhou China"},{"name":"School of Electronic Information Engineering Beihang University  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,2]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ADICS58448.2024.10533619"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"e_1_2_10_5_1","unstructured":"X.Zhu W.Su L.Lu B.Li X.Wang andJ.Dai \u201cDeformable DETR: Deformable Transformers for End\u2010to\u2010End Object Detection \u201d2020. arXiv Preprint arXiv:2010.04159."},{"key":"e_1_2_10_6_1","first-page":"16965","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Zhao Y.","year":"2024"},{"key":"e_1_2_10_7_1","unstructured":"Y.Chen \u201cConvolutional Neural Network for Sentence Classification \u201d (Master's Thesis University of Waterloo 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Y.","year":"2024"},{"key":"e_1_2_10_21_1","first-page":"107984","article-title":"Yolov10: Real\u2010Time End\u2010To\u2010End Object Detection","volume":"37","author":"Wang A.","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_10_22_1","unstructured":"Z.Yao J.Ai B.Li andC.Zhang \u201cEfficient Detr: Improving End\u2010To\u2010End Object Detector With Dense Prior \u201d (2021). arXiv Preprint arXiv:2104.01318."},{"key":"e_1_2_10_23_1","first-page":"13711","volume-title":"Proceedings of the 2024 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)","author":"Shi S.","year":"2024"},{"key":"e_1_2_10_24_1","doi-asserted-by":"crossref","unstructured":"H.Zhang K.Liu Z.Gan andG. 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