用开源工具从第一视角航拍视频中提取无人机遥测数据，精准又省算力。
VORTEX: A Spatial Computing Framework for Optimized Drone Telemetry Extraction from First-Person View Flight Data
- 用OCR+图像增强技术从飞行画面中识别仪表盘数据
- 每5秒采样一次可省80.5%算力，速度误差仅4.2%
- 适合做无人机数据自动化分析的研究者和工程师
本文提出视觉光学识别遥测提取系统（VORTEX），用于从第一人称视角（FPV）无人航空系统（UAS）录像中提取与分析无人机遥测数据。VORTEX采用基于PyTorch的MMOCR光学字符识别（OCR）工具箱，结合CLAHE增强与自适应阈值等图像预处理技术，从无人机平视显示器（HUD）画面中提取遥测变量。研究系统考察了不同时间采样率（1秒、5秒、10秒、15秒、20秒）与坐标处理方法对空间精度与计算效率的影响。结果表明，5秒采样率仅使用4.07%可用帧数，即可实现64%的数据点保留率，且平均速度精度相比1秒基线仅差4.2%，同时降低80.5%计算开销。坐标处理对比显示，UTM Zone 33N投影与哈弗辛公式结果差异小于0.1%，而原始WGS84坐标会使距离低估15-30%，速度低估20-35%。高度测量对采样率变化表现出意外稳健性，各间隔间仅2.1%波动。本研究首次提供量化基准，建立基于开源工具与空间库的无人机遥测提取分析可靠框架。
原文摘要 · Abstract (English)
This paper presents the Visual Optical Recognition Telemetry EXtraction (VORTEX) system for extracting and analyzing drone telemetry data from First Person View (FPV) Uncrewed Aerial System (UAS) footage. VORTEX employs MMOCR, a PyTorch-based Optical Character Recognition (OCR) toolbox, to extract telemetry variables from drone Heads Up Display (HUD) recordings, utilizing advanced image preprocessing techniques, including CLAHE enhancement and adaptive thresholding. The study optimizes spatial accuracy and computational efficiency through systematic investigation of temporal sampling rates (1s, 5s, 10s, 15s, 20s) and coordinate processing methods. Results demonstrate that the 5-second sampling rate, utilizing 4.07% of available frames, provides the optimal balance with a point retention rate of 64% and mean speed accuracy within 4.2% of the 1-second baseline while reducing computational overhead by 80.5%. Comparative analysis of coordinate processing methods reveals that while UTM Zone 33N projection and Haversine calculations provide consistently similar results (within 0.1% difference), raw WGS84 coordinates underestimate distances by 15-30% and speeds by 20-35%. Altitude measurements showed unexpected resilience to sampling rate variations, with only 2.1% variation across all intervals. This research is the first of its kind, providing quantitative benchmarks for establishing a robust framework for drone telemetry extraction and analysis using open-source tools and spatial libraries.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。