{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T16:12:40Z","timestamp":1779898360137,"version":"3.53.1"},"reference-count":70,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,9,24]],"date-time":"2020-09-24T00:00:00Z","timestamp":1600905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National key R and D plan on strategic international scientific and technological innovation cooperation special project","award":["2016YFE0202300"],"award-info":[{"award-number":["2016YFE0202300"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61671332, 41771452, 51708426, 41890820 and 41771454"],"award-info":[{"award-number":["61671332, 41771452, 51708426, 41890820 and 41771454"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Natural Science Fund of Hubei Province in China","award":["2018CFA007"],"award-info":[{"award-number":["2018CFA007"]}]},{"name":"the Independent Research Projects of Wuhan University","award":["2042018kf0250"],"award-info":[{"award-number":["2042018kf0250"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Object detection in Unmanned Aerial Vehicle (UAV) images plays fundamental roles in a wide variety of applications. As UAVs are maneuverable with high speed, multiple viewpoints, and varying altitudes, objects in UAV images are distributed with great heterogeneity, varying in size, with high density, bringing great difficulty to object detection using existing algorithms. To address the above issues, we propose a novel global density fused convolutional network (GDF-Net) optimized for object detection in UAV images. We test the effectiveness and robustness of the proposed GDF-Nets on the VisDrone dataset and the UAVDT dataset. The designed GDF-Net consists of a Backbone Network, a Global Density Model (GDM), and an Object Detection Network. Specifically, GDM refines density features via the application of dilated convolutional networks, aiming to deliver larger reception fields and to generate global density fused features. Compared with base networks, the addition of GDM improves the model performance in both recall and precision. We also find that the designed GDM facilitates the detection of objects in congested scenes with high distribution density. The presented GDF-Net framework can be instantiated to not only the base networks selected in this study but also other popular object detection models.<\/jats:p>","DOI":"10.3390\/rs12193140","type":"journal-article","created":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T01:39:33Z","timestamp":1600997973000},"page":"3140","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":80,"title":["Object Detection in UAV Images via Global Density Fused Convolutional Network"],"prefix":"10.3390","volume":"12","author":[{"given":"Ruiqian","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenfeng","family":"Shao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4323-382X","authenticated-orcid":false,"given":"Xiao","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaming","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deren","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, R., Li, H., Duan, K., You, S., Liu, K., Wang, F., and Hu, Y. 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