OGCF-YOLO: a resolution-preserving and neck-reconstructed lightweight detector for small wildlife in UAV imagery

Low-altitude unmanned aerial vehicle (UAV) remote sensing constitutes a pivotal paradigm for ecological monitoring; however, the detection of small wildlife targets is persistently hampered by fine-grained feature dissipation and semantic aliasing inherent to dense distributions and complex aerial backgrounds. To address these bottlenecks, we propose OGCF-YOLO, a lightweight, high-precision object detection network tailored for UAV edge deployment. Using YOLOv8n as a baseline, the method achieves a deep synergy between detection accuracy and computational efficiency by reconstructing feature transmission links and optimizing fusion mechanisms. The primary improvements include: (1) structural reallocation of computational resources: The removal of the redundant deep P5 branch and the introduction of a high-resolution P2 detection head to physically suppress the premature loss of small target information; (2) structured gated fusion architecture: the design of the C2f-OGCF module to reconstruct the neck network, where OrthoMix performs numerically stabilized channel-pair mixing and the gated competitive mechanism alleviates semantic conflicts during cross-scale fusion; (3) feature purification mechanism: the integration of the spatial and channel synergistic attention module at the end of the backbone to enhance feature separability for dense targets in complex backgrounds; and (4) content-aware dynamic upsampling: the integration of the DySample module to replace traditional interpolation, significantly improving fine-grained texture recovery capabilities. Experiments on the Animal Object Detection dataset demonstrate that OGCF-YOLO achieves a performance leap while substantially reducing computational overhead. Compared with the baseline model, the parameter count is reduced by 69.9% (to 0.77M), whereas precision, recall, mAP50, and mAP50:95 are improved by 3.9%, 12.9%, 14.1%, and 8.5%, respectively, reaching 85.5%, 70.5%, 80.4%, and 42.6%. Crucially, the model attains a single-frame inference latency of 104 ms on an NVIDIA Jetson Nano via TensorRT INT8, validating its efficacy as a high-recall solution for real-time ecological monitoring on resource-constrained UAV endpoints.