arXiv:2609.39756v1 Announce Type: new
Abstract: This article introduces an algorithm that uses a U-Net architecture to determine vertical ground surface displacements from unmanned aerial vehicle (UA...
By Wojciech Gruszczy\'nski, Edyta Puniach, Pawe{\l} \'Cwi\k{a}ka{\l}a, Wojciech Matwij
The paper tackles the problem of calibrating a stationary camera’s yaw, pitch, and roll using a drone’s GPS trajectory, which suffers from altitude bias, time offset, and discrete sampling. It formulates a parameter estimation problem that jointly estimates the GPS altitude bias, time offset, and camera orientation biases, and proposes a maximum likelihood estimator based on Iterated Least Squares to handle the asynchronous, discrete GPS data. Simulation results show the estimator achieves accuracy close to the Cramér–Rao Lower Bound, with a recommended drone trajectory yielding calibration errors within 14% of the measurement error standard deviation.
By R. Yang, Y. Bar-Shalom, H. A. J. Huang
arXiv:2608. 06404v1 Announce Type: cross Abstract: Accurate 3D crop monitoring underpins data-driven precision agriculture by enabling field-scale analysis of plant structure, growth dynamics, and management response.
By Junxiong Zhou, Xuechen Li, Chonghao Qiu, Lang Qiao, Xiaowei Jia, Qi Yang, Chishan Zhang, Leikun Yin, Nanshan You, Vipin Kumar, David Mulla, Ce Yang, Zhenong Jin, Licheng Liu
The paper presents a multi‑campaign UAV thermal image dataset for inert ordnance screening, comprising 5,855 labeled image pairs collected in Tennessee across diverse terrains and seasons. The authors trained YOLOV11l and RT‑DETR‑R50 models on 33 m and 15 m altitude data, achieving automated candidate detection, and provided practical guidelines for future humanitarian mine action surveys. The dataset and models aim to aid screening and prioritization for follow‑up technical surveys or EOD assessment, not to replace clearance operations.
By Chad Melton, PhD., Annabelle Kelton
arXiv:2609.12521v1 Announce Type: new
Abstract: Unmanned aerial vehicle facade inspection can combine red, green, and blue (RGB) imagery with thermal measurements to screen surface and subsurface ano...
By Yuan Yang, Shulei Li, Haobo Liang
The paper presents MM‑IQA, a lightweight no‑reference image quality assessment framework designed for UAV imaging. It fuses interpretable metrics—blur, edge structure, low‑resolution artifacts, exposure imbalance, noise, haze, and frequency content—to output a single quality score between 0 and 100. Evaluated on five benchmark datasets, MM‑IQA achieved SRCC values from 0.647 to 0.830 and runs in about 1.97 s per image with modest memory usage.
By Koffi Titus Sergio Aglin, Anthony K. Muchiri, Celestin Nkundineza