{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T05:45:57Z","timestamp":1773899157906,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T00:00:00Z","timestamp":1657497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Animal Welfare Innovation Award of the InitiativeTierwohl"},{"name":"Gesellschaft zur F\u00f6rderung des Tierwohls in der Nutztierhaltung mbH"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Injurious pecking against conspecifics is a serious problem in turkey husbandry. Bloody injuries act as a trigger mechanism to induce further pecking, and timely detection and intervention can prevent massive animal welfare impairments and costly losses. Thus, the overarching aim is to develop a camera-based system to monitor the flock and detect injuries using neural networks. In a preliminary study, images of turkeys were annotated by labelling potential injuries. These were used to train a network for injury detection. Here, we applied a keypoint detection model to provide more information on animal position and indicate injury location. Therefore, seven turkey keypoints were defined, and 244 images (showing 7660 birds) were manually annotated. Two state-of-the-art approaches for pose estimation were adjusted, and their results were compared. Subsequently, a better keypoint detection model (HRNet-W48) was combined with the segmentation model for injury detection. For example, individual injuries were classified using \u201cnear tail\u201d or \u201cnear head\u201d labels. Summarizing, the keypoint detection showed good results and could clearly differentiate between individual animals even in crowded situations.<\/jats:p>","DOI":"10.3390\/s22145188","type":"journal-article","created":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T03:50:36Z","timestamp":1657597836000},"page":"5188","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2870-9954","authenticated-orcid":false,"given":"Nina","family":"Volkmann","sequence":"first","affiliation":[{"name":"Science and Innovation for Sustainable Poultry Production (WING), University of Veterinary Medicine Hannover, Foundation, 49377 Vechta, Germany"},{"name":"Institute for Animal Hygiene, Animal Welfare and Animal Behavior, University of Veterinary Medicine Hannover, Foundation, 30173 Hannover, Germany"}]},{"given":"Claudius","family":"Zelenka","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Engineering, Christian-Albrechts-University, 24118 Kiel, Germany"}]},{"given":"Archana Malavalli","family":"Devaraju","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Engineering, Christian-Albrechts-University, 24118 Kiel, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5118-145X","authenticated-orcid":false,"given":"Johannes","family":"Br\u00fcnger","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Engineering, Christian-Albrechts-University, 24118 Kiel, Germany"}]},{"given":"Jenny","family":"Stracke","sequence":"additional","affiliation":[{"name":"Institute of Animal Science, Farm Animal Ethology, University of Bonn, 53115 Bonn, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8638-0718","authenticated-orcid":false,"given":"Birgit","family":"Spindler","sequence":"additional","affiliation":[{"name":"Institute for Animal Hygiene, Animal Welfare and Animal Behavior, University of Veterinary Medicine Hannover, Foundation, 30173 Hannover, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0092-4302","authenticated-orcid":false,"given":"Nicole","family":"Kemper","sequence":"additional","affiliation":[{"name":"Institute for Animal Hygiene, Animal Welfare and Animal Behavior, University of Veterinary Medicine Hannover, Foundation, 30173 Hannover, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4398-1569","authenticated-orcid":false,"given":"Reinhard","family":"Koch","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Engineering, Christian-Albrechts-University, 24118 Kiel, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.compag.2017.11.032","article-title":"Development of an early warning algorithm to detect sick broilers","volume":"144","author":"Zhuang","year":"2018","journal-title":"Comput. 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