NEB-YOLO: An Improved Lightweight YOLOv8 Network for Fine-Grained Bird Recognition in Complex Environments

Birds, as essential components of biodiversity, serve as critical indicators of ecosystem health and stability through their population dynamics and spatial distribution. However, the complexity of natural habitats, occlusion caused by avian behavior, and logistical challenges in field monitoring pose significant difficulties for fine-grained bird detection. To address these issues, this paper presents NEB-YOLO, a lightweight bird detection network built upon an improved YOLOv8 (You Only Look Once version 8) architecture. First, to enhance detection capability for fine-grained bird images in complex backgrounds, an Efficient Multi-scale Attention (EMA) mechanism is integrated into the Neck network of the YOLOv8n architecture to construct the teacher network. Second, to accommodate resource constraints in practical scenarios, channel-wise Group_Slim pruning is applied to reduce the parameter count and computational overhead of the student model. Third, to achieve high accuracy with lightweight models, a joint offline distillation approach is adopted that combines logic-based knowledge distillation (BCKD) with feature-based knowledge distillation (CWD). This design facilitates effective transfer of discriminative features for bird imagery from the teacher model to the student model. Experiments demonstrate that the proposed framework achieves a performance gain of over 1.3% in mAP on the fine-grained CUB-200-2011 bird dataset, while reducing model size by 67% compared to the original model. These results confirm that NEB-YOLO strikes an effective balance between model size and accuracy, indicating its suitability for resource-constrained scenarios.