IECE Transactions on Intelligent Systematics Home About Editors Current Issue
-
CiteScore
2.14
Impact Factor
  1. Home
  2. Journals
  3. IECE Transactions on Intelligent Systematics
  4. Volume 2, Issue 2
MFE-YOLO: A Multi-feature Fusion Algorithm for Airport Bird Detection
Research Article | Published: 21 May 2025
IECE Transactions on Intelligent Systematics Volume 2, Issue 2, 2025: 85-94
Volume 2, Issue 2, IECE Transactions on Intelligent Systematics
Volume 2, Issue 2, 2025
Submit Manuscript Edit a Special Issue
Academic Editor
Bernard De Baets
Bernard De Baets
Ghent University, Belgium
Article QR Code
Article QR Code
Scan the QR code for reading
Popular articles
Research on A Ship Trajectory Classification Method Based on Deep Learning YOLOv7-Bw: A Dense Small Object Efficient Detector Based on Remote Sensing Image Bridging Modalities: A Survey of Cross-Modal Image-Text Retrieval A Mimic Fusion Algorithm for Dual Channel Video Based on Possibility Distribution Synthesis Theory Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data Visual Feature Extraction and Tracking Method Based on Corner Flow Detection Inaugural Editorial of the Chinese Journal of Information Fusion Simultaneous Spatiotemporal Bias Compensation and Data Fusion for Asynchronous Multisensor Systems YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems Short and Long-Term Renewable Electricity Demand Forecasting Based on CNN-Bi-GRU Model
IECE Transactions on Intelligent Systematics, Volume 2, Issue 2, 2025: 85-94

Free to Read | Research Article | 21 May 2025
MFE-YOLO: A Multi-feature Fusion Algorithm for Airport Bird Detection
by
Hui Sun Hui Sun 1
Yukun Wang Yukun Wang 1
Jingjin Du Jingjin Du 1
Rui Wang Rui Wang 1 *
1 School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
* Corresponding Author: Rui Wang, [email protected]
DOI: 10.62762/TIS.2025.323887
Received: 28 March 2025, Accepted: 25 April 2025, Published: 21 May 2025  
   PDF  (5.38 MB) Full-Text HTML XML
Article Metrics Cite This Article
Abstract
To address the issues of low accuracy in manual observation and slow detection by radar in airport bird detection, this paper designs a lightweight bird detection network named MFE-YOLOv8. This network is based on the YOLOv8 framework, with the main body part featuring an MF module replacing the original C2f module to enhance the network's feature extraction capability. An EMA mechanism is added to increase the focus on bird targets, and the Focal-Modulation module is introduced to reduce background interference. Additionally, a DCSlideLoss is designed during the supervised network training process to alleviate the imbalance of samples. Finally, the real-time detection performance is verified by combining the Byte Track algorithm, and generalization experiments are conducted on the public MS COCO dataset. The experimental results show that the MFE-YOLO algorithm has certain improvements in evaluation metrics such as Precision, Recall, and mean Average Precision, indicating that this algorithm has good detection performance and can achieve precise detection of birds in the low-altitude area of airports.
Graphical Abstract
MFE-YOLO: A Multi-feature Fusion Algorithm for Airport Bird Detection
Keywords
airport bird detection
MFE-YOLO
multi-feature
attention mechanism
byte track algorithm
Data Availability Statement
Data will be made available on request.
Funding
This work was supported by Key Research and Development Program of Tianjin, China under Grant 22YFZCSN00210.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
References
  1. Liu, Z., Wang, Z., Cao, Y., Cai, S., Chen, W., & Lu, K. (2025). Risk assessment of bird strike at airport based on improved CRITIC-cloud model. Ecology and Environment, 34(1), 135.
    [CrossRef]   [Google Scholar]
  2. Hu, Y., Xing, P., Yang, F., Feng, G., Yang, G., & Zhang, Z. (2020). A birdstrike risk assessment model and its application at Ordos Airport, China. Scientific Reports, 10(1), 19627.
    [CrossRef]   [Google Scholar]
  3. Yu, C., Zhao, F., Yin, Y., & Wu, Y. (2025). Entropy-weighted TOPSIS-based bird strike risk assessment for an airport. International Journal of Computational Intelligence Systems, 18(1), 28.
    [CrossRef]   [Google Scholar]
  4. Gong, J., Yan, J., Kong, D., & Li, D. (2024, September). Enhancing airport airspace safety: A small radar system for mitigating bird, drone, and wake vortex hazards. In 2024 AIAA DATC/IEEE 43rd Digital Avionics Systems Conference (DASC) (pp. 1-10). IEEE.
    [CrossRef]   [Google Scholar]
  5. Zadeh, A. T., Mälzer, M., Nguyen, D. H., Moll, J., & Krozer, V. (2021, March). Radar-based detection of birds at wind turbines: Numerical analysis for optimum coverage. In 2021 15th European Conference on Antennas and Propagation (EuCAP) (pp. 1-5). IEEE.
    [CrossRef]   [Google Scholar]
  6. Shakeri, M., & Zhang, H. (2012, July). Real-time bird detection based on background subtraction. In Proceedings of the 10th World Congress on Intelligent Control and Automation (pp. 4507-4510). IEEE.
    [CrossRef]   [Google Scholar]
  7. Boudaoud, L. B., Maussang, F., Garello, R., & Chevallier, A. (2019, June). Marine bird detection based on deep learning using high-resolution aerial images. In OCEANS 2019-Marseille (pp. 1-7). IEEE.
    [CrossRef]   [Google Scholar]
  8. Wang, R., Li, J., Shi, Y., & Sun, H. (2022). Vision-based path planning algorithm of unmanned bird-repelling vehicles in airports. Journal of Beijing University of Aeronautics and Astronautics, 50(5), 1446-1453.
    [CrossRef]   [Google Scholar]
  9. Yang, F., Fan, H., Chu, P., Blasch, E., & Ling, H. (2019). Clustered object detection in aerial images. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 8311-8320).
    [Google Scholar]
  10. Wang, H., Liu, C., Cai, Y., Chen, L., & Li, Y. (2024). YOLOv8-QSD: An improved small object detection algorithm for autonomous vehicles based on YOLOv8. IEEE Transactions on Instrumentation and Measurement.
    [CrossRef]   [Google Scholar]
  11. Yang, C., Huang, Z., & Wang, N. (2022). QueryDet: Cascaded sparse query for accelerating high-resolution small object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 13668-13677).
    [CrossRef]   [Google Scholar]
  12. Wu, T., Tang, S., Zhang, R., Cao, J., & Zhang, Y. (2020). CGNet: A light-weight context guided network for semantic segmentation. IEEE Transactions on Image Processing, 30, 1169-1179.
    [CrossRef]   [Google Scholar]
  13. Ouyang, D., He, S., Zhang, G., Luo, M., Guo, H., Zhan, J., & Huang, Z. (2023, June). Efficient multi-scale attention module with cross-spatial learning. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
    [CrossRef]   [Google Scholar]
  14. Yang, J., Li, C., Dai, X., & Gao, J. (2022). Focal modulation networks. Advances in Neural Information Processing Systems, 35, 4203-4217.
    [Google Scholar]
  15. Amirgaliyev, B., Mussabek, M., Rakhimzhanova, T., & Zhumadillayeva, A. (2025). A review of machine learning and deep learning methods for person detection, tracking and identification, and face recognition with applications. Sensors, 25(5), 1410.
    [CrossRef]   [Google Scholar]
  16. Zou, Z., Chen, K., Shi, Z., Guo, Y., & Ye, J. (2023). Object detection in 20 years: A survey. Proceedings of the IEEE, 111(3), 257-276.
    [CrossRef]   [Google Scholar]
  17. Cheng, G., Yuan, X., Yao, X., Yan, K., Zeng, Q., Xie, X., & Han, J. (2023). Towards large-scale small object detection: Survey and benchmarks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(11), 13467-13488.
    [CrossRef]   [Google Scholar]
  18. Wang, C., Shen, H. Z., Fan, F., Shao, M. W., Yang, C. S., Luo, J. C., & Deng, L. J. (2021). EAA-Net: A novel edge assisted attention network for single image dehazing. Knowledge-Based Systems, 228, 107279.
    [CrossRef]   [Google Scholar]
  19. Wang, C. Y., Bochkovskiy, A., & Liao, H. Y. M. (2023). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7464-7475).
    [CrossRef]   [Google Scholar]
Cite This Article
APA Style
Sun, H., Wang, Y., Du, J., & Wang, R. (2025). MFE-YOLO: A Multi-feature Fusion Algorithm for Airport Bird Detection. IECE Transactions on Intelligent Systematics, 2(2), 85–94. https://doi.org/10.62762/TIS.2025.323887
Article Metrics
Citations:

Google Scholar
0

Crossref

0

Scopus

0

Web of Science

0
Article Access Statistics:
Views: 174
PDF Downloads: 55
Publisher's Note
IECE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Institute of Emerging and Computer Engineers (IECE) or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
IECE Transactions on Intelligent Systematics

IECE Transactions on Intelligent Systematics

ISSN: 2998-3355 (Online) | ISSN: 2998-3320 (Print)

Email: [email protected]

Portico

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/iece/

Copyright © 2025 Institute of Emerging and Computer Engineers Inc.