DAFS-YOLO: Dense-Aware Feature Enhancement for UAV-Based Grassland Livestock Detection in Dense Sheep Scenes

Automatic detection of grassland livestock from unmanned aerial vehicle (UAV) imagery can support livestock resource surveys and grazing management. However, compared with larger livestock such as cattle and horses, sheep in wide-field images are typically small, densely distributed, closely spaced, and easily confused with similar background textures, resulting in missed detections, background false positives, and duplicate detections. To address these challenges, this study presents DAFS-YOLO for UAV-based livestock detection in dense sheep scenes. A Dense-Aware Shallow Convolution module (DASConv) enhances shallow-feature discriminability for small-scale sheep in dense scenes, reducing missed detections and background false positives. A Local Spatial–Semantic Complementary Mapping module (LSCM) preserves richer shallow spatial information during feature propagation, improving small-object localization. Gaussian Soft-NMS optimizes the selection of highly overlapping candidate boxes in dense sheep regions and reduces duplicate detections. Experiments on a self-constructed UAV livestock dataset show that DAFS-YOLO achieves mAP50 and mAP50:95 values of 0.930 and 0.634, outperforming YOLOv11n by 4.4 and 4.8 percentage points, respectively. The corresponding sheep-class values are 0.922 and 0.564. With only 2.65 M parameters, the model also demonstrates good cross-dataset generalization on SheepCounter, providing an effective solution for intelligent UAV-based livestock monitoring.