{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T16:18:37Z","timestamp":1792858717039,"version":"4.5.0"},"reference-count":28,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T00:00:00Z","timestamp":1780531200000},"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":["61263040"],"award-info":[{"award-number":["61263040"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62262043"],"award-info":[{"award-number":["62262043"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"award":["61263040"],"award-info":[{"award-number":["61263040"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"award":["62262043"],"award-info":[{"award-number":["62262043"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["202408360176"],"award-info":[{"award-number":["202408360176"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"award":["202408360176"],"award-info":[{"award-number":["202408360176"]}],"id":[{"id":"https:\/\/ror.org\/04atp4p48","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>UAV-based vehicle detection faces challenges of small targets, dense distribution, and occlusions. Built upon YOLOv10n, this paper proposes YOLOv10-IAD by integrating three modules: (1) Involution convolution in the backbone to enlarge the receptive field and enhance spatial perception for small targets; (2) ACmix (Attention and Convolution Mixed) in the neck to fuse local details with global context; (3) DyHead (Dynamic Head) that recalibrates features via scale-, space-, and task-aware attention, improving localization for occluded objects. On VisDrone2019 and UAVDT datasets, YOLOv10-IAD improves mAP50 by 3.7% (to 47.2%) and 3.5% (to 52.0%), and recall by 3.1% and 2.0%, respectively, with only a modest increase in parameters (2.9 M) and computational cost. Compared to other YOLO series, it achieves a favorable trade-off between detection accuracy and computational efficiency. These advancements make it suitable for deployment on hardware onboard UAVs for real-time road vehicle detection.<\/jats:p>","DOI":"10.3390\/s26113585","type":"journal-article","created":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T17:11:04Z","timestamp":1780593064000},"page":"3585","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Vehicle Detection in Drone Aerial Views Based on Lightweight YOLOv10-IAD"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-8761-8805","authenticated-orcid":false,"given":"Lei","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0530-3484","authenticated-orcid":false,"given":"Zhongmin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3902-4045","authenticated-orcid":false,"given":"Yufeng","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Engineering, University of the West of England, Bristol BS16 1QY, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,4]]},"reference":[{"key":"ref_1","unstructured":"Yang, J. 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