Deep Learning and Fuzzy Logic-Based Hybrid Framework for Aerial Object Tracking

Abstract

This study introduces an aerial target detection and tracking system using the YOLOv8 model combined with the ByteTrack algorithm. The proposed system is built on improving accuracy and efficiency in the detection and tracking of aerial objects in video frames. Detection is performed using a specially trained YOLOv8 model. ByteTrack further completes the tracking in a robust manner, even for dynamically changing environments. The presented scheme also embeds a new decision-making layer by using a Fuzzy Logic System. This system adopts Trapezoidal Membership Functions for confidence and distance evaluation of detected objects. It enables the framework to achieve priority levels in tracking. For prediction of future positions, a Kalman Filter is applied. This enhances the ability of the system to foresee various situations. Different scenarios effectively demonstrate how the system can dynamically prioritize and track multiple objects in accordance with their threat level and proximity. The findings of this study could contribute to existing methods by improving detection and tracking accuracy. They also incorporate a decision-making process like humans. Hence, its highly applicable domains are defense and surveillance, which require real-time and accurate threat assessment. This system can operate reliably under different operational conditions and may provide a valid tool for enhancing airspace security.

Keywords

• Aerial target detection
• Object tracking
• Deep learning
• ByteTrack
• Fuzzy logic
• Kalman filter

References

1. Ahmadi, H., Gholamzadeh, M., Shahmoradi, L., Nilashi, M., & Rashvand, P. (2018). Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review. Computer Methods and Programs in Biomedicine, 161, 145–172. https://doi.org/10.1016/j.cmpb.2018.04.013
2. Airshow Highlights - Cleveland National Air Show, (2023). www.youtube.com/watch?v=RNxCeOD4kMA
3. Akyüz, B., Bahadır, M., & İnik, Ö. (2025). Multiple object detection and tracking in real-time aerial imagery with deep learning architectures. International Journal of Multidisciplinary Studies and Innovative Technologies, 9(1), 162–172.
4. Ali, M. L., & Zhang, Z. (2024). The YOLO framework: A comprehensive review of evolution, applications, and benchmarks in object detection. Computers, 13(12), 336. https://doi.org/10.3390/computers13120336
5. Boyd, K., Eng, K. H., & Page, C. D. (2013). Area under the precision-recall curve: Point estimates and confidence intervals. Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, 451–466. Lecture Notes in Computer Science(), vol 8190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40994-3_29
6. Brookner, E. (1998). Tracking and Kalman filtering made easy. John Wiley & Sons.
7. Bukhsh, F. A., Bukhsh, Z. A., & Daneva, M. (2020). A systematic literature review on requirement prioritization techniques and their empirical evaluation. Computer Standards & Interfaces, 69, 103389. https://doi.org/10.1016/j.csi.2019.103389
8. Cai, Y., Luan, T., Gao, H., Wang, H., Chen, L., Li, Y., Sotelo, M. A., & Li, Z. (2021). YOLOv4-5D: An effective and efficient object detector for autonomous driving. IEEE Transactions on Instrumentation and Measurement, 70, 1–13. https://doi.org/10.1109/TIM.2021.3065438

9. Carneiro, T., Da Nóbrega, R. V. M., Nepomuceno, T., Bian, G. B., De Albuquerque, V. H. C., & Rebouças Filho, P. P. (2018). Performance analysis of Google Colaboratory as a tool for accelerating deep learning applications. IEEE Access, 6, 61677–61685. https://doi.org/10.1109/ACCESS.2018.2874767
10. Celikyilmaz, A., & Turksen, I. B. (2009). Modeling uncertainty with fuzzy logic. Springer. https://doi.org/10.1007/978-3-540-89924-2
11. De Silva, C. W. (2018). Intelligent control: Fuzzy logic applications. CRC Press. https://doi.org/10.1201/9780203750513
12. Diwan, T., Anirudh, G., & Tembhurne, J. V. (2023). Object detection using YOLO: Challenges, architectural successors, datasets and applications. Multimedia Tools and Applications, 82, 9243–9275. https://doi.org/10.1007/s11042-022-13644-y
13. Faragher, R. (2012). Understanding the basis of the Kalman filter via a simple and intuitive derivation [Lecture Notes]. IEEE Signal Processing Magazine, 29(5), 128–132. https://doi.org/10.1109/MSP.2012.2203621
14. Granqvist, S., & Hammarberg, B. (2003). The correlogram: A visual display of periodicity. The Journal of the Acoustical Society of America, 114(5), 2934–2945. https://doi.org/10.1121/1.1590972
15. Gunjal, P. R., Gunjal, B. R., Shinde, H. A., Vanam, S. M., & Aher, S. S. (2018). Moving object tracking using Kalman filter. 2018 International Conference on Advances in Communication and Computing Technology (ICACCT), 544–547. https://doi.org/10.1109/ICACCT.2018.8529402
16. Hamid, K. R., Talukder, A., & Islam, A. K. M. E. (2018). Implementation of fuzzy aided Kalman filter for tracking a moving object in two-dimensional space. International Journal of Fuzzy Logic and Intelligent Systems, 18(2), 85–96. https://doi.org/10.5391/IJFIS.2018.18.2.85
17. Hao, Z., Zhang, D., & Honarvar Shakibaei Asli, B. (2024). Motion prediction and object detection for image-based visual servoing systems using deep learning. Electronics, 13(17), 3487. https://doi.org/10.3390/electronics13173487
18. Huang, Y., Zhang, Y., Wu, Z., Li, N., & Chambers, J. (2017). A novel adaptive Kalman filter with inaccurate process and measurement noise covariance matrices. IEEE Transactions on Automatic Control, 63(2), 594–601. https://doi.org/10.1109/TAC.2017.2730480
19. Hun, L. C., Yeng, O. L., Sze, L. T., Chet, K. V., (2016). Kalman filtering and its real-time applications. Real-time Systems, 93–116.
20. Jain, A., & Sharma, A. (2020). Membership function formulation methods for fuzzy logic systems: A comprehensive review. Journal of Critical Reviews, 7(19), 8717–8733.
21. Jawaharlalnehru, A., Sambandham, T., Sekar, V., Ravikumar, D., Loganathan, V., Kannadasan, R., Khan, A. A., Wechtaisong, C., Haq, M. A., Alhussen, A., & Nayyar, A. (2022). Target object detection from unmanned aerial vehicle (UAV) images based on improved YOLO algorithm. Electronics, 11(15), 2343. https://doi.org/10.3390/electronics11152343
22. Kaggle. (2024). Military aircraft detection dataset. https://www.kaggle.com/datasets/a2015003713/militaryaircraftdetectiondataset
23. KalmanFilter.net. (2024). Kalman filter multi summary. https://www.kalmanfilter.net/multiSummary.html
24. Kang, K., Li, H., Xiao, T., Ouyang, W., Yan, J., Liu, X., & Wang, X. (2017). Object detection in videos with tubelet proposal networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 727–735.
25. Kateb, F. A., Monowar, M. M., Hamid, M. A., Ohi, A. Q., & Mridha, M. F. (2021). FruitDet: Attentive feature aggregation for real-time fruit detection in orchards. Agronomy, 11(12), 2440. https://doi.org/10.3390/agronomy11122440
26. Kaymakcı, Z. E., Akarsu, M., & Ozturk, C. N. (2023). Multiple small-scale object detection in aerial vehicle images using standard or optimized YOLO detectors. 2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA), 1–5. https://doi.org/10.1109/INISTA59065.2023.10310562
27. Kulesz, P. A., Francis, D. F., & Barr, C. D. (2016). Multi-panel scatter plots and scatter plot matrices. http://www.scsug.org/wp-content/uploads/2012/11/Multi-Panel-Scatter-Plots-and-Scatter-Plot-Matrices1.pdf
28. Leśniak, A., Danek, T., & Wojdyła, M. (2009). Application of Kalman filter to noise reduction in multichannel data. Schedae Informaticae, 17(18), 63–73.
29. Pulli, K., Baksheev, A., Kornyakov, K., & Eruhimov, V. (2012). Real-time computer vision with OpenCV. Communications of the ACM, 55(6), 61–69. https://doi.org/10.1145/2184319.2184337
30. Rasheed, A. F., & Zarkoosh, M. (2025). Optimized YOLOv8 for multi-scale object detection. Journal of Real-Time Image Processing, 22(1), 6.
31. Shao, Y., Yang, Z., Li, Z., & Li, J. (2024). Aero-YOLO: An efficient vehicle and pedestrian detection algorithm based on unmanned aerial imagery. Electronics, 13(7), 1190. https://doi.org/10.3390/electronics13071190
32. Shen, J. Q., Yuan, D., Yang, Z., Yan, J., Xiao, B., & Xing, X. (2022). YOLO network-based drogue recognition method for autonomous aerial refueling. Journal of Northwestern Polytechnical University, 40(4), 787-795.
33. Singh, P. R., Gottumukkala, R., & Maida, A. (2025). An analysis of Kalman filter based object tracking methods for fast-moving tiny objects. arXiv preprint arXiv:2509.18451.
34. Sohan, M., Sai Ram, T., & Rami Reddy, C.V. (2024). A review on YOLOv8 and its advancements. International Conference on Data Intelligence and Cognitive Informatics, 529–545. https://doi.org/10.1007/978-981-99-7962-2_39
35. Susmaga, R. (2004). Confusion matrix visualization. Intelligent Information Processing and Web Mining, 107–116. https://doi.org/10.1007/978-3-540-39985-8_12
36. Vasanthi, P., & Mohan, L. (2024). Multi-head-self-attention based YOLOv5x-transformer for multi-scale object detection. Multimedia Tools and Applications, 83, 36491–36517. https://doi.org/10.1007/s11042-023-15773-4
37. Vujović, Ž. (2021). Classification model evaluation metrics. International Journal of Advanced Computer Science and Applications, 12(6). https://doi.org/10.14569/IJACSA.2021.0120670
38. Welch, G. F. (2021). Kalman filter. Computer Vision: A Reference Guide, 1-3, Springer International Publishing. https://doi.org/10.1007/978-3-030-03243-2_716-1
39. Xu, X., & Wu, X. (2023). Target recognition algorithm for UAV aerial images based on improved YOLO-X. 2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology (ICCASIT), 83–87. https://doi.org/10.1109/ICCASIT58768.2023.10351552
40. Zhang, H., Bai, X., Pan, Y., Wang, Q., & Zheng, H. (2023). Design of an unmanned platform electro-optical tracking system based on YOLOv4 and OpenCV image recognition algorithm. 2023 International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), 457–463. https://doi.org/10.1109/AEECA59734.2023.00087
41. Zhang, M., Xie, T., Wang, C., & Zheng, K. (2022). Aerial orthophoto target recognition and positioning method based on improved YOLOX. 2022 15th International Symposium on Computational Intelligence and Design (ISCID), 102–105. https://doi.org/10.1109/ISCID56505.2022.00030
42. Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W., & Wang, X. (2022). ByteTrack: Multi-object tracking by associating every detection box. European Conference on Computer Vision (ECCV), 1–21. https://doi.org/10.1007/978-3-031-20047-2_1
43. Zhang, Y., Zhang, H., Huang, Q., Han, Y., & Zhao, M. (2024). DSP-YOLO: An anchor-free network with DSPAN for small object detection of multiscale defects. Expert Systems with Applications, 241, 122669. https://doi.org/10.1016/j.eswa.2023.122669
44. Zhao, X., Zhang, W., Xia, Y., Zhang, H., Zheng, C., Ma, J., & Zhang, Z. (2024). G-YOLO: A lightweight infrared aerial remote sensing target detection model for UAVs based on YOLOv8. Drones, 8(9), 495. https://doi.org/10.3390/drones8090495
45. Zhao, Z. Q., Zheng, P., Xu, S., & Wu, X. (2019). Object detection with deep learning: A review. IEEE Transactions on Neural Networks and Learning Systems, 30(11), 3212–3232. https://doi.org/10.1109/TNNLS.2018.2876865
46. Zong, C., Meng, K., Sun, J., & Zhou, Q. (2023). Real time object recognition based on YOLO model. 2023 3rd International Conference on Electronic Information Engineering and Computer Science (EIECS), 197–202. https://doi.org/10.1109/EIECS59936.2023.10435392