{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T14:48:30Z","timestamp":1779288510882,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2019,8,29]],"date-time":"2019-08-29T00:00:00Z","timestamp":1567036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["No 696231"],"award-info":[{"award-number":["No 696231"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"name":"German Federal Ministry of Food and Agriculture","award":["2817ERA08D"],"award-info":[{"award-number":["2817ERA08D"]}]},{"DOI":"10.13039\/501100001862","name":"Svenska Forskningsr\u00e5det Formas","doi-asserted-by":"publisher","award":["Dnr 2017-00152"],"award-info":[{"award-number":["Dnr 2017-00152"]}],"id":[{"id":"10.13039\/501100001862","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Posture detection targeted towards providing assessments for the monitoring of health and welfare of pigs has been of great interest to researchers from different disciplines. Existing studies applying machine vision techniques are mostly based on methods using three-dimensional imaging systems, or two-dimensional systems with the limitation of monitoring under controlled conditions. Thus, the main goal of this study was to determine whether a two-dimensional imaging system, along with deep learning approaches, could be utilized to detect the standing and lying (belly and side) postures of pigs under commercial farm conditions. Three deep learning-based detector methods, including faster regions with convolutional neural network features (Faster R-CNN), single shot multibox detector (SSD) and region-based fully convolutional network (R-FCN), combined with Inception V2, Residual Network (ResNet) and Inception ResNet V2 feature extractions of RGB images were proposed. Data from different commercial farms were used for training and validation of the proposed models. The experimental results demonstrated that the R-FCN ResNet101 method was able to detect lying and standing postures with higher average precision (AP) of 0.93, 0.95 and 0.92 for standing, lying on side and lying on belly postures, respectively and mean average precision (mAP) of more than 0.93.<\/jats:p>","DOI":"10.3390\/s19173738","type":"journal-article","created":{"date-parts":[[2019,8,29]],"date-time":"2019-08-29T11:26:22Z","timestamp":1567077982000},"page":"3738","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":133,"title":["Deep Learning and Machine Vision Approaches for Posture Detection of Individual Pigs"],"prefix":"10.3390","volume":"19","author":[{"given":"Abozar","family":"Nasirahmadi","sequence":"first","affiliation":[{"name":"Department of Agricultural and Biosystems Engineering, University of Kassel, 37213 Witzenhausen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Barbara","family":"Sturm","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biosystems Engineering, University of Kassel, 37213 Witzenhausen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8890-0112","authenticated-orcid":false,"given":"Sandra","family":"Edwards","sequence":"additional","affiliation":[{"name":"School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Knut-H\u00e5kan","family":"Jeppsson","sequence":"additional","affiliation":[{"name":"Department of Biosystems and Technology, Swedish University of Agricultural Sciences, 23053 Alnarp, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anne-Charlotte","family":"Olsson","sequence":"additional","affiliation":[{"name":"Department of Biosystems and Technology, Swedish University of Agricultural Sciences, 23053 Alnarp, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simone","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"Department Animal Husbandry, Thuringian State Institute for Agriculture and Rural Development, 07743 Jena, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oliver","family":"Hensel","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biosystems Engineering, University of Kassel, 37213 Witzenhausen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.livsci.2017.05.014","article-title":"Implementation of machine vision for detecting behaviour of cattle and pigs","volume":"202","author":"Nasirahmadi","year":"2017","journal-title":"Livestock Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0168-1699(00)00129-0","article-title":"The development and evaluation of image analysis procedures for guiding a livestock monitoring sensor placement robot","volume":"28","author":"Frost","year":"2000","journal-title":"Comput. 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