{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T13:35:57Z","timestamp":1773581757927,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T00:00:00Z","timestamp":1703376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>This study delves into the vital missions of the armed forces, encompassing the defense of territorial integrity, sovereignty, and support for civil institutions. Commanders grapple with crucial decisions, where accountability underscores the imperative for reliable field intelligence. Harnessing artificial intelligence, specifically, the YOLO version five detection algorithm, ensures a paradigm of efficiency and precision. The presentation of trained models, accompanied by pertinent hyperparameters and dataset specifics derived from public military insignia videos and photos, reveals a nuanced evaluation. Results scrutinized through precision, recall, map@0.5, mAP@0.95, and F1 score metrics, illuminate the supremacy of the model employing Stochastic Gradient Descent at 640 \u00d7 640 resolution: 0.966, 0.957, 0.979, 0.830, and 0.961. Conversely, the suboptimal performance of the model using the Adam optimizer registers metrics of 0.818, 0.762, 0.785, 0.430, and 0.789. These outcomes underscore the model\u2019s potential for military object detection across diverse terrains, with future prospects considering the implementation on unmanned arial vehicles to amplify and deploy the model effectively.<\/jats:p>","DOI":"10.3390\/info15010011","type":"journal-article","created":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T20:42:30Z","timestamp":1703450550000},"page":"11","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Military Decision-Making Process Enhanced by Image Detection"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8635-4285","authenticated-orcid":false,"given":"Nikola","family":"\u017diguli\u0107","sequence":"first","affiliation":[{"name":"Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3121-2228","authenticated-orcid":false,"given":"Matko","family":"Glu\u010dina","sequence":"additional","affiliation":[{"name":"Department of Engineering, Istrian University of Applied Sciences, Riva 6, 52100 Pula, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5964-245X","authenticated-orcid":false,"given":"Ivan","family":"Lorencin","sequence":"additional","affiliation":[{"name":"Department of Engineering, Istrian University of Applied Sciences, Riva 6, 52100 Pula, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dario","family":"Matika","sequence":"additional","affiliation":[{"name":"Vara\u017edin University Center, University North, 31b Jurja Kri\u017eani\u0107a St., 42000 Vara\u017edin, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,24]]},"reference":[{"key":"ref_1","unstructured":"Headquarters Deparment of the Army (2022). 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