{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T04:47:05Z","timestamp":1792039625356,"version":"4.4.0"},"reference-count":38,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jilin Province Science and Technology Department","award":["20240101335JC"],"award-info":[{"award-number":["20240101335JC"]}],"id":[{"id":"https:\/\/ror.org\/049x38272","id-type":"ROR","asserted-by":"crossref"}]},{"award":["20240101335JC"],"award-info":[{"award-number":["20240101335JC"]}],"id":[{"id":"https:\/\/ror.org\/049x38272","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Animals"],"abstract":"<jats:p>Real-time machine-vision monitoring of group-housed juvenile sika deer is challenging because targets are often small, densely distributed and partially occluded, while edge deployment requires compact models. This study developed a lightweight system for non-contact behavior recognition under commercial farm-pen conditions. A fixed-camera dataset was constructed with five annotated categories: resting, standing, feeding, social and abnormal. The dataset was limited to one month of recordings from one fixed camera monitoring six juvenile sika deer in a single pen at one farm in Jilin Province, China. To reduce background interference during training, a Multi-target Bionic Foveal Augmentation (MBFA) strategy was proposed to generate target-guided, spatially nonuniform degradation based on the distribution of deer instances, without introducing additional inference cost. A Lightweight Selective Alignment Reorganization and Efficient Detection Network (LSARE-Net) was then designed to enhance local behavioral cues, contextual feature representation, and cross-scale fusion. On the test set, LSARE-Net achieved 91.26% recall, 93.47% mAP@0.5 and 80.24% mAP@0.5:0.95 with 0.70 M parameters and a 1.7 MB model size. After TensorRT conversion, it reached 62.64 FPS on an NVIDIA Jetson AGX Orin. These results demonstrate the feasibility of compact frame-level behavior detection under the evaluated pen conditions. The accompanying prototype organizes detections into behavioral summaries and abnormal-cue records for manual review.<\/jats:p>","DOI":"10.3390\/ani16193094","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T18:43:24Z","timestamp":1790880204000},"page":"3094","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LSARE-Net: Target-Guided Augmentation and Lightweight Edge Detection for Real-Time Behavior Monitoring of Captive Juvenile Sika Deer"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1590-9055","authenticated-orcid":false,"given":"Xipeng","family":"Li","sequence":"first","affiliation":[{"name":"College of Information Technology, Jilin Agricultural University, Changchun 130118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"He","family":"Pan","sequence":"additional","affiliation":[{"name":"Information Center, Jilin Agricultural University, Changchun 130118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3526-9037","authenticated-orcid":false,"given":"Adnan","family":"Mahmood","sequence":"additional","affiliation":[{"name":"School of Computing, Macquarie University, Sydney, NSW 2109, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8999-8967","authenticated-orcid":false,"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Technology, Jilin Agricultural University, Changchun 130118, China"},{"name":"School of Computing, Macquarie University, Sydney, NSW 2109, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tong","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Information Technology, Jilin Agricultural University, Changchun 130118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuncheng","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Information Technology, Jilin Agricultural University, Changchun 130118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,10,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ge, G., Cheng, Z., Xu, J., Yue, B., Hou, H., Liu, Y., Zhu, C., Ge, H., Shi, S., and Li, J. 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