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. 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 est
The increasing proliferation of UAVs in various sectors and their growing role in strategic applications necessitates robust detection and tracking capabilities, especially in adverse weather conditions.
Reliable UAV detection and tracking in challenging environments like fog is critical for both military and civilian applications, impacting national security, air traffic control, and public safety.
This research provides a framework to improve the accuracy and resilience of UAV surveillance systems against environmental degradation, addressing a significant operational vulnerability.
- · Defence contractors
- · Surveillance technology companies
- · Border security agencies
- · AI/ML developers
- · Adversaries relying on UAV stealth
- · Criminal organizations using drones
Improved situational awareness for critical infrastructure protection and military operations through enhanced UAV detection.
Reduced operational costs and risks for drone-based activities in foggy conditions due to more reliable tracking systems preventing accidents or loss.
Accelerated development of autonomous drone navigation and counter-drone systems, making airspace more controlled and secure globally.
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