DRG-LiteStar-YOLO: Reliability-Guided Pseudo-Depth Fusion for Dairy Goat Detection in Complex Barns

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±0.002 mAP@0.5:0.95 compared with 0.697±0.004 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.