{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T14:50:44Z","timestamp":1774018244230,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:00:00Z","timestamp":1773964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Young Talents of Basic Research in Universities of Heilongjiang Province","award":["YQJH2024077"],"award-info":[{"award-number":["YQJH2024077"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Detecting small unmanned aerial vehicles (UAVs) in complex airspace presents significant challenges due to their minimal pixel footprint, resemblance to birds, and frequent occlusion. To address these issues, we propose YOLOv11-ResCBAM, a novel real-time detection framework that integrates a Residual Convolutional Block Attention Module (ResCBAM) and a high-resolution P2 detection head into the YOLOv11 architecture. ResCBAM enhances channel and spatial feature refinement while preserving original feature contexts through residual connections, and the P2 head maintains fine spatial details crucial for small-object localization. Evaluated on a custom dataset of 4917 images (11,733 after augmentation) across three classes (drone, bird, airplane), our model achieves a mean average precision at the 0.5\u20130.95 IoU threshold (mAP@0.5\u20130.95) of 0.845, representing a 7.9% improvement over the baseline YOLOv11n, while maintaining real-time inference at 50.51 FPS. Cross-dataset validation on VisDrone2019-DET and UAVDT benchmarks demonstrates promising generalization trends. This work demonstrates the effectiveness of the proposed approach for UAV surveillance systems, balancing detection accuracy with computational efficiency for deployment in security-critical environments.<\/jats:p>","DOI":"10.3390\/jimaging12030140","type":"journal-article","created":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T12:58:53Z","timestamp":1774011533000},"page":"140","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Real-Time Small UAV Detection in Complex Airspace Using YOLOv11 with Residual Attention and High-Resolution Feature Enhancement"],"prefix":"10.3390","volume":"12","author":[{"given":"Chuang","family":"Han","sequence":"first","affiliation":[{"name":"Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin 150080, China"},{"name":"Sunny Group Co., Ltd., Yuyao 315400, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3500-5732","authenticated-orcid":false,"given":"Md Redwan","family":"Ullah","sequence":"additional","affiliation":[{"name":"Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin 150080, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amrul","family":"Kayes","sequence":"additional","affiliation":[{"name":"School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 610031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khalid","family":"Hasan","sequence":"additional","affiliation":[{"name":"Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin 150080, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md Abdur","family":"Rouf","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300130, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md Rakib","family":"Hasan","sequence":"additional","affiliation":[{"name":"Department of Marketing, Begum Rokeya University, Rangpur 5400, Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shen","family":"Tao","sequence":"additional","affiliation":[{"name":"Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin 150080, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guo","family":"Gengli","sequence":"additional","affiliation":[{"name":"Sunny Group Co., Ltd., Yuyao 315400, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Masum","family":"Billah","sequence":"additional","affiliation":[{"name":"Higher Educational Key Laboratory for Measuring, Control Technology, and Instrumentation of Heilongjiang Province, Harbin 150080, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1109\/COMST.2023.3312221","article-title":"Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey","volume":"26","author":"Kurunathan","year":"2023","journal-title":"IEEE Commun. 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