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<p>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&#xa0;million parameters while maintaining high detection efficiency. It not only surpasses the baseline YOLOv8n (72.8% mAP@0.5 with 3.1&#xa0;M parameters) in accuracy but also outperforms the larger YOLOv8s model (74.4% mAP@0.5 with 11.1&#xa0;M 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.</p>

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Design and implementation of a fine-grained detection method for Qinghai Lake birds via fusion of knowledge guidance and feature enhancement

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月9日

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  • Yongkang Zhang,
  • GuiLian Feng,
  • ShengLei Pei

摘要

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 3.1 M parameters) in accuracy but also outperforms the larger YOLOv8s model (74.4% mAP@0.5 with 11.1 M 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.