Work

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

Year: 2026

Type: conference-paper

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.

DOI: 10.66073/refocus-conf/0910275

Authors: Utegenova Aliya Timurovna, Abdigappar Kydyrbek Seitzhapparuly

Institution: International Taraz University named after Sherkhan Murtaza

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

OpenAlex ID: W7225001317

FWCI:

Cites: 3

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Related to: 10