{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T15:27:27Z","timestamp":1788535647199,"version":"build-2803163510"},"reference-count":45,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T00:00:00Z","timestamp":1788480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioinform."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Mounting behaviour in group-housed pigs poses significant welfare and productivity challenges, often resulting in injuries and stress. To address this issue, we developed a novel computer vision-based approach for the automatic detection of mounting behaviour in pigs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The pilot study was conducted on eight boars, aged 4\u20135 months and weighing between 60 and 90 kg, housed in two groups of four. Continuous video footage was collected over a 1-week period using an overhead camera, resulting in 766 2-s video clips. The proposed hybrid approach integrates Mask Region-based Convolutional Neural Network (Mask R-CNN) instance segmentation with a binary classifier. To evaluate the model\u2019s performance under varying data conditions, the dataset was divided into training, validation, and testing sets using three data grouping scenarios: by frame, by clip, and by day.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among the five evaluated binary classification algorithms, Support Vector Machine (SVM) was selected based on its superior performance. The hybrid system achieved a balanced accuracy and F1-score of 95% with the clip-based split, rising to 99% with the frame-based split and falling to 74% with the day-based split.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Under pilot conditions, the system offers a feasible proof-of-concept for continuous monitoring of mounting behaviour in group-housed pigs, operating at a processing rate of five frames per second. This capability offers strong potential for supporting early intervention and proactive welfare management, subject to future multi-site validation. Moving forward, future research will focus on integrating individual animal tracking and conducting larger-scale studies to further explore the system\u2019s scalability and enhance its performance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fbinf.2026.1869902","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T15:05:46Z","timestamp":1788534346000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Identifying mounting behaviour in boars using deep learning-based instance segmentation and binary classification - a pilot study"],"prefix":"10.3389","volume":"6","author":[{"given":"Harry","family":"Aricibasi","sequence":"first","affiliation":[{"name":"Department of Animal Biosciences, University of Guelph","place":["Guelph, ON, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessica","family":"Bode","sequence":"additional","affiliation":[{"name":"Department of Animal Biosciences, University of Guelph","place":["Guelph, ON, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ren\u00e9e","family":"Bergeron","sequence":"additional","affiliation":[{"name":"Department of Animal Biosciences, University of Guelph","place":["Guelph, ON, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Tulpan","sequence":"additional","affiliation":[{"name":"Department of Animal Biosciences, University of Guelph","place":["Guelph, ON, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,4]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000006","article-title":"Learning deep architectures for AI","volume":"2","author":"Bengio","year":"2009","journal-title":"FNT Mach. 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