A Task-Driven Evaluation of UAV Detection and Tracking under Synthetic Fog
This paper presents a task-driven framework linking synthetic fog generation, image restoration, detection, and tracking. Results show fog degrades performance mainly through missed detections; fog-inclusive training offers the most consistent robustness gains.
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[Submitted on 6 Jul 2026]
Title:A Task-Driven Evaluation of UAV Detection and Tracking under Synthetic Fog
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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. This paper presents a task-driven evaluation framework that links depth-aware synthetic fog generation, image restoration, object detection, and tracking within a unified pipeline. Given the practical difficulty of collecting and annotating foggy UAV scenes, synthetic fog is generated from real clear-weather outdoor images containing UAV targets using monocular depth estimation and the atmospheric scattering model. Representative restoration methods from classical, convolutional neural network (CNN)-based, and transformer-based families are first compared, after which the selected restoration model is integrated into the downstream perception pipeline. Detection is evaluated under both clean-only and fog-inclusive training regimes using multiple detector variants, while tracking-by-detection is assessed on clean, foggy, and restored video sequences. Beyond image-level restoration metrics, the study evaluates how fog and restoration affect detection robustness and tracking performance. The results show that fog substantially degrades both detection and tracking, primarily through increased missed detections. Fog-inclusive training provides the most consistent improvement in robustness, whereas test-time restoration is most beneficial when the detector has been trained only on clean imagery. These findings show that restoration quality does not necessarily translate into proportional gains in downstream perception and therefore should be evaluated jointly with detection and tracking performance.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
Cite as: arXiv:2607.05467 [cs.CV]
(or arXiv:2607.05467v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.05467
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Vesal Ahsani [view email] [v1] Mon, 6 Jul 2026 07:06:37 UTC (4,824 KB)
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