DSCF-DET: An RT-DETR-Based Framework for Fine-Grained Detection of Musk Deer and Visually Similar Artiodactyls

Musk deer are forest-dwelling artiodactyls of high conservation value, but their wild populations remain under severe conservation pressure due to poaching driven by the demand for natural musk, together with habitat fragmentation and habitat loss. Efficient non-invasive image-based monitoring is therefore important for musk deer conservation; however, fine-grained detection of musk deer and visually similar artiodactyls in ecological images remains difficult because of background camouflage, vegetation occlusion, and high inter-class similarity. In this study, a fine-grained wildlife image dataset was constructed, and an RT-DETR-based framework, termed DSCF-DET, was proposed for automated detection in complex natural scenes. DSCF-DET integrates three task-oriented modules: DRPBlock for receptive-field-aware feature extraction, SASTE for sparse spatial encoding, and CBAFusion for cross-level feature fusion. On the constructed dataset, DSCF-DET achieved 91.4% precision, 86.2% recall, 88.7% F1-score, and 86.4% mAP50. Compared with RT-DETR-r18, it improved these metrics by 8.6, 12.1, 10.5, and 12.4 percentage points, respectively, while maintaining moderate model complexity. Visualization results showed more target-focused feature responses and reduced background-related activations. Cross-dataset experiments on an independent public wildlife dataset further suggested potential applicability to broader wildlife detection scenarios. These results indicate that DSCF-DET provides a computationally balanced approach for ecological image screening and intelligent musk deer monitoring.