{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T16:35:14Z","timestamp":1787675714940,"version":"build-2784847793"},"reference-count":27,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T00:00:00Z","timestamp":1710115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Conservation, Missouri"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Waterfowl populations monitoring is essential for wetland conservation. Lately, deep learning techniques have shown promising advancements in detecting waterfowl in aerial images. In this paper, we present performance evaluation of several popular supervised and semi-supervised deep learning models for waterfowl detection in aerial images using four new image datasets containing 197,642 annotations. The best-performing model, Faster R-CNN, achieved 95.38% accuracy in terms of mAP. Semi-supervised learning models outperformed supervised models when the same amount of labeled data was used for training. Additionally, we present performance evaluation of several deep learning models on waterfowl classifications on aerial images using a new real-bird classification dataset consisting of 6,986 examples and a new decoy classification dataset consisting of about 10,000 examples per category of 20 categories. The best model achieved accuracy of 91.58% on the decoy dataset and 82.88% on the real-bird dataset.<\/jats:p>","DOI":"10.3390\/info15030157","type":"journal-article","created":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T07:25:32Z","timestamp":1710141932000},"page":"157","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Deep Learning Models for Waterfowl Detection and Classification in Aerial Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7785-4836","authenticated-orcid":false,"given":"Yang","family":"Zhang","sequence":"first","affiliation":[{"name":"The Department of Electrical Engineering and Computer Science (EECS), University of Missouri, Columbia, MO 65201, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Feng","sequence":"additional","affiliation":[{"name":"The Department of Electrical Engineering and Computer Science (EECS), University of Missouri, Columbia, MO 65201, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiqi","family":"Wang","sequence":"additional","affiliation":[{"name":"The Department of Electrical Engineering and Computer Science (EECS), University of Missouri, Columbia, MO 65201, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhicheng","family":"Tang","sequence":"additional","affiliation":[{"name":"The Department of Electrical Engineering and Computer Science (EECS), University of Missouri, Columbia, MO 65201, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenduo","family":"Zhai","sequence":"additional","affiliation":[{"name":"The Department of Electrical Engineering and Computer Science (EECS), University of Missouri, Columbia, MO 65201, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reid","family":"Viegut","sequence":"additional","affiliation":[{"name":"The School of Natural Resources, University of Missouri, Columbia, MO 65201, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3851-6056","authenticated-orcid":false,"given":"Lisa","family":"Webb","sequence":"additional","affiliation":[{"name":"U.S. Geological Survey, Missouri Cooperative Fish and Wildlife Research Unit, University of Missouri, Columbia, MO 65201, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Raedeke","sequence":"additional","affiliation":[{"name":"The Missouri Department of Conservation, Columbia, MO 65201, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7771-4034","authenticated-orcid":false,"given":"Yi","family":"Shang","sequence":"additional","affiliation":[{"name":"The Department of Electrical Engineering and Computer Science (EECS), University of Missouri, Columbia, MO 65201, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,11]]},"reference":[{"key":"ref_1","unstructured":"Missouri Department of Conservation (2019). 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