{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T22:52:17Z","timestamp":1776207137395,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,28]],"date-time":"2022-05-28T00:00:00Z","timestamp":1653696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61703287"],"award-info":[{"award-number":["61703287"]}]},{"name":"National Natural Science Foundation of China","award":["LJKZ0218"],"award-info":[{"award-number":["LJKZ0218"]}]},{"name":"National Natural Science Foundation of China","award":["JYT2020045"],"award-info":[{"award-number":["JYT2020045"]}]},{"name":"National Natural Science Foundation of China","award":["RC210401"],"award-info":[{"award-number":["RC210401"]}]},{"name":"National Natural Science Foundation of China","award":["2020JH2\/10100045"],"award-info":[{"award-number":["2020JH2\/10100045"]}]},{"name":"Scientific Research Program of Liaoning Provincial Education Department of China","award":["61703287"],"award-info":[{"award-number":["61703287"]}]},{"name":"Scientific Research Program of Liaoning Provincial Education Department of China","award":["LJKZ0218"],"award-info":[{"award-number":["LJKZ0218"]}]},{"name":"Scientific Research Program of Liaoning Provincial Education Department of China","award":["JYT2020045"],"award-info":[{"award-number":["JYT2020045"]}]},{"name":"Scientific Research Program of Liaoning Provincial Education Department of China","award":["RC210401"],"award-info":[{"award-number":["RC210401"]}]},{"name":"Scientific Research Program of Liaoning Provincial Education Department of China","award":["2020JH2\/10100045"],"award-info":[{"award-number":["2020JH2\/10100045"]}]},{"name":"Young and Middle-aged Science and Technology Innovation Talents Project of Shenyang of China","award":["61703287"],"award-info":[{"award-number":["61703287"]}]},{"name":"Young and Middle-aged Science and Technology Innovation Talents Project of Shenyang of China","award":["LJKZ0218"],"award-info":[{"award-number":["LJKZ0218"]}]},{"name":"Young and Middle-aged Science and Technology Innovation Talents Project of Shenyang of China","award":["JYT2020045"],"award-info":[{"award-number":["JYT2020045"]}]},{"name":"Young and Middle-aged Science and Technology Innovation Talents Project of Shenyang of China","award":["RC210401"],"award-info":[{"award-number":["RC210401"]}]},{"name":"Young and Middle-aged Science and Technology Innovation Talents Project of Shenyang of China","award":["2020JH2\/10100045"],"award-info":[{"award-number":["2020JH2\/10100045"]}]},{"name":"Liaoning Provincial Key R&amp;D Program of China","award":["61703287"],"award-info":[{"award-number":["61703287"]}]},{"name":"Liaoning Provincial Key R&amp;D Program of China","award":["LJKZ0218"],"award-info":[{"award-number":["LJKZ0218"]}]},{"name":"Liaoning Provincial Key R&amp;D Program of China","award":["JYT2020045"],"award-info":[{"award-number":["JYT2020045"]}]},{"name":"Liaoning Provincial Key R&amp;D Program of China","award":["RC210401"],"award-info":[{"award-number":["RC210401"]}]},{"name":"Liaoning Provincial Key R&amp;D Program of China","award":["2020JH2\/10100045"],"award-info":[{"award-number":["2020JH2\/10100045"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, with the rapid development of unmanned aerial vehicles (UAV) technology and swarm intelligence technology, hundreds of small-scale and low-cost UAV constitute swarms carry out complex combat tasks in the form of ad hoc networks, which brings great threats and challenges to low-altitude airspace defense. Security requirements for low-altitude airspace defense, using visual detection technology to detect and track incoming UAV swarms, is the premise of anti-UAV strategy. Therefore, this study first collected many UAV swarm videos and manually annotated a dataset named UAVSwarm dataset for UAV swarm detection and tracking; thirteen different scenes and more than nineteen types of UAV were recorded, including 12,598 annotated images\u2014the number of UAV in each sequence is 3 to 23. Then, two advanced depth detection models are used as strong benchmarks, namely Faster R-CNN and YOLOX. Finally, two state-of-the-art multi-object tracking (MOT) models, GNMOT and ByteTrack, are used to conduct comprehensive tests and performance verification on the dataset and evaluation metrics. The experimental results show that the dataset has good availability, consistency, and universality. The UAVSwarm dataset can be widely used in training and testing of various UAV detection tasks and UAV swarm MOT tasks.<\/jats:p>","DOI":"10.3390\/rs14112601","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T02:30:06Z","timestamp":1653964206000},"page":"2601","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["UAVSwarm Dataset: An Unmanned Aerial Vehicle Swarm Dataset for Multiple Object Tracking"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6737-3472","authenticated-orcid":false,"given":"Chuanyun","family":"Wang","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence, Shenyang Aerospace University, Shenyang 110136, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1805-1781","authenticated-orcid":false,"given":"Yang","family":"Su","sequence":"additional","affiliation":[{"name":"School of Computer Science, Shenyang Aerospace University, Shenyang 110136, China"}]},{"given":"Jingjing","family":"Wang","sequence":"additional","affiliation":[{"name":"China Academic of Electronics and Information Technology, Beijing 100041, China"}]},{"given":"Tian","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence, Beihang University, Beijing 100191, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8862-9257","authenticated-orcid":false,"given":"Qian","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Shenyang Aerospace University, Shenyang 110136, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,28]]},"reference":[{"key":"ref_1","first-page":"5","article-title":"Exploration of UAV cluster defense technology","volume":"12","author":"Cai","year":"2020","journal-title":"Aerodyn. 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