{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T15:54:59Z","timestamp":1790351699752,"version":"4.1.0"},"reference-count":38,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T00:00:00Z","timestamp":1779494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004829","name":"Sichuan Science and Technology Program","doi-asserted-by":"publisher","award":["2024YFNH0018"],"award-info":[{"award-number":["2024YFNH0018"]}],"id":[{"id":"10.13039\/501100004829","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004829","name":"Sichuan Science and Technology Program","doi-asserted-by":"publisher","award":["2026YFHZ0159"],"award-info":[{"award-number":["2026YFHZ0159"]}],"id":[{"id":"10.13039\/501100004829","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004829","name":"Sichuan Science and Technology Program","doi-asserted-by":"publisher","award":["2024YFHZ0088"],"award-info":[{"award-number":["2024YFHZ0088"]}],"id":[{"id":"10.13039\/501100004829","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004829","name":"Sichuan Science and Technology Program","doi-asserted-by":"publisher","award":["2023YFN0009"],"award-info":[{"award-number":["2023YFN0009"]}],"id":[{"id":"10.13039\/501100004829","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Xizang Science and Technology Department Project","award":["XZ202601ZY0078"],"award-info":[{"award-number":["XZ202601ZY0078"]}]},{"name":"Xizang Science and Technology Department Project","award":["XZ202401ZY00018"],"award-info":[{"award-number":["XZ202401ZY00018"]}]},{"name":"Sichuan Province Unmanned System Intelligent Sensing and Control Technology Engineering Laboratory Open project","award":["WRXT2023-2"],"award-info":[{"award-number":["WRXT2023-2"]}]},{"name":"Key Laboratory of Agricultural Equipment Technology for Hilly and Mountainous Areas, Ministry of Agriculture and Rural Affairs, P.R. China","award":["2024QSNZ04. 522"],"award-info":[{"award-number":["2024QSNZ04. 522"]}]},{"award":["2024QSNZ04. 522"],"award-info":[{"award-number":["2024QSNZ04. 522"]}],"id":[{"id":"https:\/\/ror.org\/05ckt8b96","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Animals"],"abstract":"<jats:p>The traditional monitoring of forest musk deer behavior primarily relies on direct human observation or the post hoc playback analysis of ordinary surveillance videos. This approach is not only time-consuming and labor-intensive but also highly subjective, easily leading to missing or misjudged critical behavioral information. Moreover, it is difficult to achieve real-time monitoring and anomaly warning. These limitations severely constrain the efficiency of the large-scale artificial breeding of forest musk deer and the effective advancement of wild population conservation. Thus, this study proposes a forest musk deer behavioral recognition method based on an improved YOLOv8-Pose. A forest musk deer behavior image dataset covering four typical behaviors was constructed, and 18 keypoints were systematically annotated. This study designs a Dilated Spatial Pyramid Pooling-Fast (DILATED-SPPF) module and a Multi-scale Depthwise Separable Context Mixer (MDSC-Mixer) module, and integrates them into YOLOv8-Pose. Experimental results show that the improved model outperforms the original YOLOv8-Pose and comparison models such as YOLOv11\/v12-Pose on key metrics of object detection (Box-mAP50 0.929, Box-mAP50-95 0.814) and pose estimation (Pose-mAP50 0.879, Pose-mAP50-95 0.565). This study further develops a visual interactive interface that intuitively presents detection results and skeleton structures. This work provides a high-precision, low-cost automated behavior analysis tool for the artificial breeding and wild conservation of forest musk deer with significant application value for enhancing the intelligence level of endangered species protection.<\/jats:p>","DOI":"10.3390\/ani16111594","type":"journal-article","created":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T00:30:33Z","timestamp":1779669033000},"page":"1594","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Keypoint-Based Forest Musk Deer Behavioral Recognition Method"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0921-7137","authenticated-orcid":false,"given":"Dequan","family":"Guo","sequence":"first","affiliation":[{"name":"School of Automation, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuankang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengli","family":"Zheng","sequence":"additional","affiliation":[{"name":"Sichuan Institute of Musk Deer Breeding (Sichuan Institute for Drug Control), Chengdu 611845, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenyu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation, Chengdu University of Information Technology, Chengdu 610225, China"},{"name":"Sichuan Chuanrongda Technology Co., Ltd., Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dapeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation, Chengdu University of Information Technology, Chengdu 610225, China"},{"name":"Sichuan Chuanrongda Technology Co., Ltd., Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dening","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Automation, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3603618","article-title":"Deep learning-based human pose estimation: A survey","volume":"56","author":"Zheng","year":"2023","journal-title":"ACM Comput. 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