{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T17:50:46Z","timestamp":1791913846485,"version":"4.3.4"},"reference-count":45,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T00:00:00Z","timestamp":1766016000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Linna Hu","award":["The High-level Talent Project by the Jinling Institute of Technology No.jit-b-202302"],"award-info":[{"award-number":["The High-level Talent Project by the Jinling Institute of Technology No.jit-b-202302"]}]},{"name":"Bin Guo","award":["The Natural Science Foundation of Xinjiang Uygur Autonomous Region, grant number:2022D01A203"],"award-info":[{"award-number":["The Natural Science Foundation of Xinjiang Uygur Autonomous Region, grant number:2022D01A203"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Detecting small objects in unmanned aerial vehicle (UAV) imagery remains a challenging task due to the limited target scale, cluttered backgrounds, severe occlusion, and motion blur commonly observed in dynamic aerial environments. This study presents STAIR-DETR, a real-time synergistic detection framework derived from RT-DETR, featuring comprehensive enhancements in feature extraction, resolution transformation, and detection head design. A Statistical Feature Attention (SFA) module is incorporated into the neck to replace the original AIFI, enabling token-level statistical modeling that strengthens fine-grained feature representation while effectively suppressing background interference. The backbone is reinforced with a Diverse Semantic Enhancement Block (DSEB), which employs multi-branch pathways and dynamic convolution to enrich semantic expressiveness without sacrificing spatial precision. To mitigate information loss during scale transformation, an Adaptive Scale Transformation Operator (ASTO) is proposed by integrating Context-Guided Downsampling (CGD) and Dynamic Sampling (DySample), achieving context-aware compression and content-adaptive reconstruction across resolutions. In addition, a high-resolution P2 detection head is introduced to leverage shallow-layer features for accurate classification and localization of extremely small targets. Extensive experiments conducted on the VisDrone2019 dataset demonstrate that STAIR-DETR attains 41.7% mAP@50 and 23.4% mAP@50:95, outperforming contemporary state-of-the-art (SOTA) detectors while maintaining real-time inference efficiency. These results confirm the effectiveness and robustness of STAIR-DETR for precise small object detection in complex UAV-based imaging scenarios.<\/jats:p>","DOI":"10.3390\/s25247681","type":"journal-article","created":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:32:03Z","timestamp":1766068323000},"page":"7681","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["STAIR-DETR: A Synergistic Transformer Integrating Statistical Attention and Multi-Scale Dynamics for UAV Small Object Detection"],"prefix":"10.3390","volume":"25","author":[{"given":"Linna","family":"Hu","sequence":"first","affiliation":[{"name":"School of Network and Communication Engineering, Jinling Institute of Technology, Nanjing 211169, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Penghao","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Jinling Institute of Technology, Nanjing 211169, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6627-3093","authenticated-orcid":false,"given":"Bin","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiwen","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Network and Communication Engineering, Jinling Institute of Technology, Nanjing 211169, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weixian","family":"Zha","sequence":"additional","affiliation":[{"name":"School of Network and Communication Engineering, Jinling Institute of Technology, Nanjing 211169, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiya","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Xinjiang Institute of Technology, Aksu 843100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,18]]},"reference":[{"key":"ref_1","unstructured":"Wu, X., Li, W., Hong, D., Tao, R., and Du, Q. 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