The paper investigates how raw or minimally processed satellite imagery affects onboard AI object detection for space missions. By systematically degrading Very High Resolution Maxar images in terms of Signal‑to‑Noise Ratio, Modulation Transfer Function, and Ground Sampling Distance, the authors evaluate three lightweight detectors—YOLOv5s, YOLOX‑S, and NanoDet—on the resulting data. Results show that image quality impacts detection performance in a degradation‑specific way, with GSD consistently shifting performance, while MTF and SNR effects vary by model and resolution; severe blur‑plus‑noise combinations cause the greatest losses.
arXiv:2608.23136v1 Announce Type: new Abstract: Poor illumination obscures small, low-contrast defects in UAV bridge imagery, reducing the reliability and operational flexibility of automated inspect...
arXiv:2608. 04720v4 Announce Type: replace Abstract: Real-time object detectors achieve remarkable accuracy under controlled conditions, yet degrade sharply on non-ideal inputs-fisheye distortion, game-rendered content, aerial views, and 360{\deg}panoramas.
Poor illumination obscures small, low-contrast defects in UAV bridge imagery, reducing the reliability and operational flexibility of automated inspection. This paper investigates whether degradation-...
arXiv:2609.37003v1 Announce Type: new Abstract: Vessel perception from space is crucial for a wide range of maritime applications, from traffic monitoring to environmental protection. However, most e...
arXiv:2610.00141v1 Announce Type: new Abstract: Vision-based anti-UAV systems must function in poor visibility, yet most benchmarks use only clear-sky footage, and previous robustness studies treat a...
arXiv:2607. 05467v1 Announce Type: cross Abstract: Fog severely degrades the visibility of small unmanned aerial vehicles (UAVs) in skydominant, long-range imagery, reducing the reliability of downstream detection and tracking.
arXiv:2609.37013v1 Announce Type: cross Abstract: Rapid assessment of building damage after natural disasters is essential to support emergency response. Earth Observation satellites can acquire rele...
Vessel perception from space is crucial for a wide range of maritime applications, from traffic monitoring to environmental protection. However, most existing datasets predominantly focus on general o...
arXiv:2609.25500v1 Announce Type: new Abstract: Training data quantity and quality greatly affect object detection model performance, regardless of model architecture. When using object detection mod...
Infrared small target detection (IRSTD) is important for low-altitude perception, unmanned-system warning, and security monitoring. However, weak targets in infrared imagery usually occupy only a few pixels and are easily submerged by cloud clutter, ground edges, and bright noise, making it difficult for lightweight segmentation-based methods to preserve local target structures while suppressing background interference.
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.
arXiv:2609.14560v1 Announce Type: new Abstract: General-purpose object detectors lose accuracy on UAV footage, where targets span only a handful of pixels and onboard compute is limited. Prior work c...