| Title: |
All-Weather Drone Vision: Passive SWIR Imaging in Fog and Rain |
| Authors: |
Bessonov, Alexander; Rozanov, Aleksei; White, Richard; Suwito, Galih; Medina-Salazar, Ivonne; Lutfullin, Marat; Gusev, Dmitrii; Shikov, Ilya |
| Source: |
Drones , 9 (8) , Article 553. (2025) |
| Publisher Information: |
MDPI AG |
| Publication Year: |
2025 |
| Collection: |
University College London: UCL Discovery |
| Subject Terms: |
SWIR imaging; visibility enhancement; all-weather drones; UAV; autonomous navigation; fog; rain |
| Description: |
Short-wave-infrared (SWIR) imaging can extend drone operations into fog and rain, yet the optimum spectral strategy remains unclear. We evaluated a drone-borne quantum-dot SWIR camera inside a climate-controlled tunnel that generated calibrated advection fog, radiation fog, and rain. Images were captured with a broadband 400–1700 nm setting and three sub-band filters, each at four lens apertures (f/1.8–5.6). Entropy, structural-similarity index (SSIM), and peak signal-to-noise ratio (PSNR) were computed for every weather–aperture–filter combination. Broadband SWIR consistently outperformed all filtered configurations. The gain stems from higher photon throughput, which outweighs the modest scattering reduction offered by narrowband selection. Under passive illumination, broadband SWIR therefore represents the most robust single-camera choice for unmanned aerial vehicles (UAVs), enhancing situational awareness and flight safety in fog and rain. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
https://discovery.ucl.ac.uk/id/eprint/10219534/ |
| Availability: |
https://discovery.ucl.ac.uk/id/eprint/10219534/1/drones-09-00553.pdf; https://discovery.ucl.ac.uk/id/eprint/10219534/ |
| Rights: |
open |
| Accession Number: |
edsbas.79F625AB |
| Database: |
BASE |