Vision-Based Detection for Aerial Intruders in Airport Flight Areas

doi:10.1007/s12204-026-2900-6

Abstract

Abstract: 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-birddrone 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.

Key words: airport aerial intruder, small object detection, real-time detection Transformer (RT-DETR), feature enhancement, tri-module fusion

摘要： 针对机场飞行区鸟类、无人机等空域入侵物存在目标尺寸微小、运动轨迹复杂多变、背景噪声干扰强烈，且需在远距离实现实时精准检测的技术难题，本文提出基于RT-DETR框架的三模融合空域检测网络(ACE-AirDETR)。通过三大核心模块实现性能突破：利用跨尺度边缘信息增强(CSEIE)模块强化目标轮廓细节，生成高区分度特征，从而显著改善小目标运动模糊导致的检测精度下降问题；引入高效加法注意力机制(EAA)优化计算效率，以提升模型实时性与可部署性；提出上下文引导的空间特征重建金字塔(CSRFPN)模块，增强对复杂背景下小目标的特征表达能力，从而有效降低误检与漏检率。为验证方法在特定场景下的有效性，本文构建了类机场环境空域入侵物ABD数据集。实验结果表明：相较RT-DETR算法，ACE-AirDETR在AP50和AP50-95指标上分别提高3.2和1.5百分点，帧率提升11.8%，同时计算量和参数量分别降低20.7%和27.3%，并实现检测精度、速度与模型轻量化的协同优化。

关键词: 机场空域入侵物，小目标检测，RT-DETR，特征增强，三模融合

CLC Number:

Niu Guochen, Lü Zhihao. Vision-Based Detection for Aerial Intruders in Airport Flight Areas[J]. J Shanghai Jiaotong Univ Sci, 2026, 31(1): 176-186.

References

[1] Zhao B, Wang N, Fu Q, et al. Searching a site for a civil airport based on bird ecological conservation: An expert-based selection (Dalian, China) [J]. Global Ecology and Conservation, 2019, 20: e00729.
[2] Qiu D X. Overview of the development of the uav industry in the low-altitude economy [J]. Business and Globalization, 2024, 12: 117-122 (in Chinese).
[3] International Civil Aviation Organization. Doc 9137, Airport services manual, part 3—wildlife hazard management [M]. 5th ed. Montréal: ICAO, 2020.
[4] Zhao J, Zhang J S, Li D D, et al. Vision-based anti-UAV detection and tracking [J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(12): 25323-25334.
[5] Fu H X, Wang C Y, Benani N, et al. Bird detection and overall bird situational awareness at airports [J]. Ornithology Research, 2024, 32(4): 280-295.
[6] Voulodimos A, Doulamis N, Doulamis A, et al. Deep learning for computer vision: A brief review [J]. Computational Intelligence and Neuroscience, 2018, 2018: 7068349.
[7] Cui Y Q, Zhou W R, Mao Z P, et al. Real-time anti-UAV early warning system based on intelligent visual detection and semantic analysis [J/OL]. Journal of Nanjing University of Information Science & Technology, 2025. https://doi.org/10.13878/j.cnki.jnuist.20250402005 (in Chinese).
[8] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
[9] Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection [C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 779-788.
[10] Bodla N, Singh B, Chellappa R, et al. Soft-NMS: Improving object detection with one line of code [C]//2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 5562-5570.
[11] Carion N, Massa F, Synnaeve G, et al. End-to-end object detection with transformers [M]//Computer vision – ECCV 2020. Cham: Springer, 2020: 213-229.
[12] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need [C]// 31st Conference on Neural Information Processing Systems. Long Beach: NIPS, 2017: 261-272.
[13] Zhu X Z, Su W J, Lu L W, et al. Deformable DETR: Deformable transformers for end-to-end object detection [PP/OL].V4. arXiv (2021-05-18) [2025-09-13]. https://arXiv.org/abs/2010.04159.
[14] Zhao Y A, Lv W Y, Xu S L, et al. DETRs beat YOLOs on real-time object detection [C]//2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2024: 16965-16974.
[15] Tang F L, Xu Z X, Huang Q M, et al. DuAT: Dual-aggregation transformer network forMedical image segmentation [M]//Pattern recognition and computer vision. Singapore: Springer, 2024: 343-356.
[16] Pan Z Z, Cai J F, Zhuang B H. Fast vision transformers with HiLo attention [PP/OL]. V5. arXiv (2023-04-19) [2025-09-13]. https://arXiv.org/abs/2205.13213.
[17] Coluccia A, Fascista A, Sommer L, et al. The drone-vs-bird detection grand challenge at ICASSP 2023: A review of methods and results [J]. IEEE Open Journal of Signal Processing, 2024, 5: 766-779.
[18] Liang Y F. Protection of civil airport clearance [J]. China Science and Technology Information, 2018(3): 28-29 (in Chinese).
[19] Varghese R, Sambath M. YOLOv8: A novel object detection algorithm with enhanced performance and robustness [C]//2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems. Chennai: IEEE, 2024: 1-6.
[20] Shaker A, Maaz M, Rasheed H, et al. SwiftFormer: Efficient additive attention for transformer-based real-time mobile vision applications [C]//2023 IEEE/CVF International Conference on Computer Vision. Paris: IEEE, 2024: 17379-17390.
[21] Yang Y M, Liu J P, Yang Y J, et al. Dual-stream fusion network with multi-head self-attention for multi-modal fake news detection [J]. Applied Soft Computing, 2024, 167: 112358.
[22] Cui Y N, Ren W Q, Knoll A. Omni-kernel network for image restoration [J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2024, 38(2), 1426-1434.
[23] Shi D. TransNeXt: Robust foveal visual perception for vision transformers [C]//2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2024: 17773-17783.