{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T16:06:38Z","timestamp":1788969998756,"version":"build-2803163510"},"reference-count":39,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research and Development Program of Shaanxi Province","award":["170445463"],"award-info":[{"award-number":["170445463"]}]},{"name":"General Program of the Shaanxi Natural Science Basic Research Program","award":["2022JM-363"],"award-info":[{"award-number":["2022JM-363"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Animals"],"abstract":"<jats:p>Reliable dairy goat detection in barns is challenging because uneven illumination, railings, and animal overlap weaken RGB boundaries. We developed DRG-LiteStar-YOLO, a lightweight RGB-camera-compatible detector that integrates monocular pseudo-depth with RGB features without requiring a dedicated depth sensor. DA3-Small was used to generate relative pseudo-depth maps, which were fused with RGB features through reliability-guided fusion and boundary-enhanced multi-scale aggregation. The dataset comprised 1521 images of 134 lactating Saanen goats and 13,985 annotated instances. On the held-out test set, DRG-LiteStar-YOLO achieved a precision of 0.961, a recall of 0.941, an mAP@0.5 of 0.978, and an mAP@0.5:0.95 of 0.735. Compared with RGB-only LiteStar-YOLO, the proposed method improved mAP@0.5 and mAP@0.5:0.95 by 1.6 and 3.8 percentage points, respectively. The five-seed cumulative ablation further showed that the full configuration achieved 0.734\u00b10.002 mAP@0.5:0.95 compared with 0.697\u00b10.004 for RGB-only LiteStar-YOLO. The detector contains 4.20 M parameters and 12.60 GFLOPs and achieves 86.2 FPS on an RTX 4090 with precomputed pseudo-depth maps. These results demonstrate that reliability-guided pseudo-depth fusion improves goat localization under complex barn conditions while retaining a compact detector design. DRG-LiteStar-YOLO provides an effective perception framework for RGB-camera-based dairy goat monitoring and supports future counting, tracking, and behavior-analysis applications.<\/jats:p>","DOI":"10.3390\/ani16182840","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T15:01:56Z","timestamp":1788966116000},"page":"2840","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DRG-LiteStar-YOLO: Reliability-Guided Pseudo-Depth Fusion for Dairy Goat Detection in Complex Barns"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6942-3266","authenticated-orcid":false,"given":"Yongliang","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Information Engineering, Northwest A&F University, Xianyang 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Keyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Northwest A&F University, Xianyang 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"resources","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Yue","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Northwest A&F University, Xianyang 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"resources","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"given":"Nan","family":"Geng","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Northwest A&F University, Xianyang 712100, China"},{"name":"Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Northwest A&F University, Xianyang 712100, China"},{"name":"Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Northwest A&F University, Xianyang 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"supervision","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.compag.2008.05.005","article-title":"Is precision livestock farming an engineer\u2019s daydream or nightmare, an animal\u2019s friend or foe, and a farmer\u2019s panacea or pitfall?","volume":"64","author":"Wathes","year":"2008","journal-title":"Comput. 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