{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T13:01:11Z","timestamp":1785416471317,"version":"3.56.0"},"reference-count":56,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["42571527"],"award-info":[{"award-number":["42571527"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"award":["42571527"],"award-info":[{"award-number":["42571527"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Unmanned aerial vehicle (UAV) small object detection faces critical challenges including irreversible geometric detail loss during multi-level downsampling, cross-scale feature distortion from interpolation blur and aliasing, and limited long-range dependency modeling due to constrained receptive fields. To address these limitations, we propose HMF-DEIM (High-Fidelity Multi-Domain Fusion Transformer for UAV Small Object Detection), an end-to-end architecture tailored for UAV small object detection. First, we design a lightweight hierarchical differentiation backbone that removes redundant deepest-layer features (P5) to prevent tiny object information loss, employing Multi-Domain Feature Blending (MDFB) in shallow layers for geometric detail preservation and a Hierarchical Attention-guided Feature Modulation Block (HAFMB) in deep layers for global semantic modeling. Second, we develop a full-chain high-fidelity feature transformation framework comprising Channel-Adaptive Shift Upsampling (CASU) for interpolation-free resolution recovery, Multi-scale Context Alignment Fusion (MCAF) for bridging deep\u2013shallow semantic gaps via bidirectional gating, and Diversified Residual Frequency-aware Downsampling (DRFD) for aliasing suppression through a three-branch parallel architecture. Finally, we devise the FocusFeature module that aligns multi-scale features to a unified scale and employs parallel multi-scale large-kernel depthwise convolutions to capture cross-scale long-range dependencies, generating dual-scale (P3\/P4) features balancing details and semantics. Experiments demonstrate that HMF-DEIM outperforms DEIM on VisDrone2019 test by 0.405 mAP50 (+2.1%) and 0.235 mAP50\u201395 (+1.6%), with a remarkable 21.3% relative improvement in APs for tiny objects, while maintaining real-time inference (465 FPS with TensorRT FP16) on an NVIDIA A100 GPU with only 11.87M parameters and 34.1 GFLOPs. Further validation on AI-TOD v2 and DOTA v1.5 datasets confirms robust generalization across diverse aerial scenarios, making it a practical solution for resource-constrained UAV applications.<\/jats:p>","DOI":"10.3390\/s26072187","type":"journal-article","created":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T14:59:01Z","timestamp":1775055541000},"page":"2187","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["HMF-DEIM: High-Fidelity Multi-Domain Fusion Transformer for UAV Small Object Detection"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9246-9727","authenticated-orcid":false,"given":"Lan","family":"Ma","sequence":"first","affiliation":[{"name":"College of International Studies, National University of Defense Technology, Nanjing 210012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1971-5324","authenticated-orcid":false,"given":"Yun","family":"Luo","sequence":"additional","affiliation":[{"name":"College of International Studies, National University of Defense Technology, Nanjing 210012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7090-1699","authenticated-orcid":false,"given":"Jiajun","family":"Xu","sequence":"additional","affiliation":[{"name":"College of International Studies, National University of Defense Technology, Nanjing 210012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"48572","DOI":"10.1109\/ACCESS.2019.2909530","article-title":"Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges","volume":"7","author":"Shakhatreh","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/MPRV.2017.11","article-title":"Help from the Sky: Leveraging UAVs for Disaster Management","volume":"16","author":"Erdelj","year":"2017","journal-title":"IEEE Pervasive Comput."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2334","DOI":"10.1109\/COMST.2019.2902862","article-title":"A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems","volume":"21","author":"Mozaffari","year":"2019","journal-title":"IEEE Commun. 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