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<p>To address the technical challenge of achieving real-time and accurate detection of aerial intruders such as birds and drones in airport flight areas, where targets are extremely small, have complex and variable trajectories, suffer from strong background noise, and require long-distance detection, a tri-module fusion airspace detection network (ACE-AirDETR) based on the real-time detection Transformer (RT-DETR) framework is proposed in this paper. Performance is enhanced through three core modules. The cross-scale edge information enhancement module strengthens target contour details, generates highly discriminative features, and significantly alleviates the decline in detection accuracy caused by motion blur of small targets. The efficient additive attention module optimizes computational efficiency and improve the model’s real-time performance and deployability. The context-guided spatial feature reconstruction feature pyramid network module enhances the feature expression capability of small targets under complex backgrounds and effectively reduces the false detection and missed detection rates. To verify the effectiveness of the proposed method in specific scenarios, a self-built airplane-bird-drone dataset for airspace intruders in airport-like environments is constructed. Experimental results show that compared with the RT-DETR algorithm, ACE-AirDETR improves the AP50 and AP50:95 metrics by 3.2 and 1.5 percentage points respectively, increases the frame rate by 11.8%, and reduces the computational complexity and parameter count by 20.7% and 27.3% respectively, achieving a coordinated optimization of detection accuracy, speed, and model lightweight.</p>

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Vision-Based Detection for Aerial Intruders in Airport Flight Areas

  • 期刊论文

  • 发表日期：

    2026年1月28日

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  • Guochen Niu,
  • Zhihao Lü

摘要

To address the technical challenge of achieving real-time and accurate detection of aerial intruders such as birds and drones in airport flight areas, where targets are extremely small, have complex and variable trajectories, suffer from strong background noise, and require long-distance detection, a tri-module fusion airspace detection network (ACE-AirDETR) based on the real-time detection Transformer (RT-DETR) framework is proposed in this paper. Performance is enhanced through three core modules. The cross-scale edge information enhancement module strengthens target contour details, generates highly discriminative features, and significantly alleviates the decline in detection accuracy caused by motion blur of small targets. The efficient additive attention module optimizes computational efficiency and improve the model’s real-time performance and deployability. The context-guided spatial feature reconstruction feature pyramid network module enhances the feature expression capability of small targets under complex backgrounds and effectively reduces the false detection and missed detection rates. To verify the effectiveness of the proposed method in specific scenarios, a self-built airplane-bird-drone dataset for airspace intruders in airport-like environments is constructed. Experimental results show that compared with the RT-DETR algorithm, ACE-AirDETR improves the AP50 and AP50:95 metrics by 3.2 and 1.5 percentage points respectively, increases the frame rate by 11.8%, and reduces the computational complexity and parameter count by 20.7% and 27.3% respectively, achieving a coordinated optimization of detection accuracy, speed, and model lightweight.