Design and Implementation of a Fine-Grained Detection Method for Qinghai Lake Birds via Fusion of Knowledge Guidance and Feature Enhancement

As a crucial high-altitude habitat and a key stopover site for migratory birds, Bird Island on Qinghai Lake requires automated monitoring for ecological conservation. In this context, bird detection poses a significant challenge due to targets' large scale variations, morphological similarities, and complex backgrounds. Current general-purpose detection models struggle to adequately perceive such fine-grained features, leading to high rates of missed and false detections in complex natural scenes. Based on the YOLOv8 architecture, this study introduces two core improvements: a High-Frequency and Spatial Dependency Perception module to enhance multi-scale feature extraction, and an adaptive knowledge-base system that incorporating ornithological knowledge to boost discrimination among similar species via knowledge-guided inference. The resulting model is named YOLO_BD (You Only Look Once_Bird Detection). Experimental results demonstrate that YOLO_BD achieves a mean Average Precision (mAP@0.5) of 75.2% with 6.6 million parameters while maintaining high detection efficiency. It not only surpasses the baseline YOLOv8n (72.8% mAP@0.5 with 3.1M parameters) in accuracy but also outperforms the larger YOLOv8s model (74.4% mAP@0.5 with 11.1M parameters), highlighting its dual advantages in achieving lightweight design and enhanced performance. This research provides an effective technical solution for intelligent wildlife monitoring in resource-constrained environments.