The impact of dynamic H.265 video compression on YOLO-based object detection algorithms for high-speed UAV navigation

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Published: 2026-10-31
DOI: https://doi.org/10.66073/ReFocus-Conf/0910275
Keywords:
UAV navigation, H.265 compression, YOLOv8, object detection, digital transmission artifacts, FPV drones, off-board AI

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Utegenova Aliya Timurovna
International Taraz University named after Sherkhan Murtaza image/svg+xml
Abdigappar Kydyrbek Seitzhapparuly
International Taraz University named after Sherkhan Murtaza image/svg+xml

Abstract

This paper examines how dynamic bitrate compression within digital video transmission frameworks for micro-UAVs (specifically, the DJI O4 Unit Pro) affects computer vision performance. We analyze the degradation in object detection accuracy (mean Average Precision, mAP) of PyTorch-based YOLOv8 models caused by H.265 encoding artifacts and system latency during high-speed aerial maneuvers. Ultimately, the empirical results provide a foundation for optimizing off-board artificial intelligence architectures.

Article Details

Issue

2026: Proceedings of the International Scientific and Practical Conference “Contribution of Women Scientists of Uzbekistan to the Development of the Country” (Nukus, October 9–10, 2026)

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Articles
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Utegenova Aliya Timurovna, & Abdigappar Kydyrbek Seitzhapparuly. (2026). The impact of dynamic H.265 video compression on YOLO-based object detection algorithms for high-speed UAV navigation. Partner Conferences of the International Scientific Journal Research Focus, 1326-1328. https://doi.org/10.66073/ReFocus-Conf/0910275

References

Jocher, G., Chaurasia, A., & Qiu, J. (2023). YOLO by Ultralytics (Version 8.0.0) [Computer software]. Ultralytics. https://github.com/ultralytics/ultralytics

Szeliski, R. (2022). Computer Vision: Algorithms and Applications (2nd ed.). Springer. DOI: https://doi.org/10.1007/978-3-030-34372-9

Macenski, S., Foote, T., Gerkey, B., Lalancette, C., & Woodall, W. (2022). Robot Operating System 2: Design, architecture, and uses in the wild. Science Robotics, 7(66), eabm6074. https://doi.org/10.1126/scirobotics.abm6074 DOI: https://doi.org/10.1126/scirobotics.abm6074

Hossain, S., & Lee, D. (2019). Deep Learning-Based Real-Time Multiple-Object Detection and Tracking from Aerial Imagery via a Flying Robot with GPU-Based Edge Computing. Sensors, 19(15), 3371. https://doi.org/10.3390/s19153371 DOI: https://doi.org/10.3390/s19153371

Bradski, G. (2000). The OpenCV Library. Dr. Dobb's Journal of Software Tools, 25(11), 120-125.

Information about the authors

Abdigappar Kydyrbek – Senior Lecturer of the Department of Information and Communication Technologies, Sherkhan Murtaza International Taraz University, Republic of Kazakhstan, 080000, Taraz, 69B Zheltoksan st.;

e-mail: abdifpv@gmail.com; ORCID: https://orcid.org/0009-0003-6907-1560.

https://t.me/Abdi_fpv

+77077550055

Utegenova Aliya – Student, Sherkhan Murtaza International Taraz University, Republic of Kazakhstan, 080000, Taraz, 69B Zheltoksan st.

e-mail: aliyautegenovs2006@gmail.com;