{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:15:34Z","timestamp":1792790134708,"version":"4.5.0"},"reference-count":40,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Xingliao Talents Plan","award":["XLYC2202013"],"award-info":[{"award-number":["XLYC2202013"]}]},{"name":"National Natural Science Foundation of Liaoning Province","award":["2025-MSLH-597"],"award-info":[{"award-number":["2025-MSLH-597"]}]},{"name":"Basic Scientific Research Project of Liaoning Provincial Department of Education","award":["LJ242610144003"],"award-info":[{"award-number":["LJ242610144003"]}]},{"name":"High-Level Talent Introduction Scientific Research Support Program of Shenyang Ligong University","id":[{"id":"https:\/\/ror.org\/03m20nr07","id-type":"ROR","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/03m20nr07","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Small-object representations in unmanned aerial vehicle (UAV) imagery are easily degraded by repeated downsampling, crowded occlusion, and background clutter. We present MF-YOLO, an accuracy-oriented detector based on YOLO11n. It introduces a stride-4 P2 prediction scale, applies Global-to-Local Spatial Aggregation (GLSA) independently to C2\u2013C5 before neck fusion, and connects P2\u2013P5 through a 64-channel Bidirectional Feature Pyramid Network (BiFPN). The contribution lies in the task-specific organization and controlled evaluation of these established components. Across three random seeds, MF-YOLO achieved 38.318 \u00b1 0.378% mean average precision at an intersection-over-union threshold of 0.5 (mAP@0.5) and 22.658 \u00b1 0.239% mAP@0.5:0.95 on the validation split, exceeding YOLO11n by 4.264 and 2.694 percentage points, respectively. In COCO-style scale evaluation, average precision for small objects (APS) increased from 8.700 \u00b1 0.092% to 12.802 \u00b1 0.115%, a relative gain of 47.2%. The parameter count decreased from 2.584 M to 2.244 M, whereas giga floating-point operations (GFLOPs) increased from 6.323 to 14.365. On an RTX 4080 SUPER, 32-bit floating-point (FP32) network-forward throughput decreased from 144.22 to 70.50 frames per second (FPS). MF-YOLO therefore improves small-object detection at the cost of substantially greater computation.<\/jats:p>","DOI":"10.3390\/s26196084","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T10:34:10Z","timestamp":1790332450000},"page":"6084","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MF-YOLO: High-Resolution Weighted Feature Fusion with Global-Local Aggregation for Small-Object Detection in UAV Imagery"],"prefix":"10.3390","volume":"26","author":[{"given":"Yufei","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"given":"Jiming","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0272-1181","authenticated-orcid":false,"given":"Fang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7380","DOI":"10.1109\/TPAMI.2021.3119563","article-title":"Detection and Tracking Meet Drones Challenge","volume":"44","author":"Zhu","year":"2022","journal-title":"IEEE Trans. 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