Synthetic Thermal Image Generation for Real-Time Animal Detection Under Low-Visibility Conditions
Wildlife-vehicle collisions remain a significant road safety concern, particularly during nighttime and low-visibility conditions when RGB-based perception systems are often unreliable. Thermal imaging offers a promising alternative for detecting animals under poor illumination. However, the limited availability of annotated infrared animal datasets restricts the development of robust deep learning-based detection models. This paper investigates synthetic thermal image generation as a scalable approach for real-time animal detection under low-visibility conditions. A subset of 514 annotated visible-spectrum animal images from the NTLNP dataset is translated into synthetic thermal represen...
| Authors | James Momoh, Khandaker Mamun Ahmed |
|---|---|
| Primary category | cs.CV |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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