{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T19:27:55Z","timestamp":1764962875983,"version":"3.46.0"},"reference-count":29,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan","award":["AP23489215)"],"award-info":[{"award-number":["AP23489215)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>This study benchmarks nine state-of-the-art object detection models on a specialized cattle dataset to assess accuracy and inference speed for real-time agricultural applications. Using a unified protocol without model-specific augmentations, and evaluating all detectors on identical RTX 4090 hardware, we provide a fair architectural comparison of two-stage, one-stage, and transformer-based models. D_FINE_L and Co_DETR_R_50 achieved the highest accuracy (AP@[0.50:0.95] = 0.872 and 0.851), while RTMDet and YOLOv11_L were the fastest (15.81 and 19.14 ms\/image). All models showed substantial accuracy gains on the domain dataset compared to COCO, while maintaining consistent relative speed rankings.<\/jats:p>","DOI":"10.3390\/a18120763","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T18:42:02Z","timestamp":1764960122000},"page":"763","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparative Analysis of Real-Time Detection Models for Intelligent Monitoring of Cattle Condition and Behavior"],"prefix":"10.3390","volume":"18","author":[{"given":"Oleg","family":"Ivashchuk","sequence":"first","affiliation":[{"name":"Department of Computer Science, Faculty of Science and Technology, Caspian University of Technology and Engineering (KUTI) Named After Sh. Yessenov, Micro District 32, Aktau 130000, Kazakhstan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1942-4474","authenticated-orcid":false,"given":"Zhanat","family":"Kenzhebayeva","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Science and Technology, Caspian University of Technology and Engineering (KUTI) Named After Sh. Yessenov, Micro District 32, Aktau 130000, Kazakhstan"}]},{"given":"Alexey","family":"Zhigalov","sequence":"additional","affiliation":[{"name":"Individual Entrepreneur Zhigalov A.A. (neural.dev), Agmashenebeli Str., 1, Batumi 6004, Adjara, Georgia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5705-2297","authenticated-orcid":false,"given":"Moldir","family":"Allaniyazova","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Science and Technology, Caspian University of Technology and Engineering (KUTI) Named After Sh. Yessenov, Micro District 32, Aktau 130000, Kazakhstan"}]},{"given":"Gulnara","family":"Kaziyeva","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Science and Technology, Caspian University of Technology and Engineering (KUTI) Named After Sh. Yessenov, Micro District 32, Aktau 130000, Kazakhstan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0826-0371","authenticated-orcid":false,"given":"Kaiyrbek","family":"Makulov","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Science and Technology, Caspian University of Technology and Engineering (KUTI) Named After Sh. Yessenov, Micro District 32, Aktau 130000, Kazakhstan"}]},{"given":"Vyacheslav","family":"Fedorov","sequence":"additional","affiliation":[{"name":"Department of Information and Robotic Systems, Belgorod State National Research University, 85, Pobedy St., Belgorod 308015, Russia"}]},{"given":"Olga","family":"Ivashchuk","sequence":"additional","affiliation":[{"name":"Department of Information and Robotic Systems, Belgorod State National Research University, 85, Pobedy St., Belgorod 308015, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17","DOI":"10.5187\/jast.2024.e111","article-title":"RGB-based machine vision for enhanced pig disease symptoms monitoring and health management: A review","volume":"67","author":"Reza","year":"2025","journal-title":"J. Anim. Sci. 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