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arXiv · 2509.23243

Increasing the Diversity in RGB-to-Thermal Image Translation for Automotive Applications

Kaili Wang·Leonardo Ravaglia·Roberto Longo·Lore Goetschalckx·David Van Hamme·Julie Moeyersoms·Ben Stoffelen·Tom De Schepper
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Abstract

Thermal imaging in Advanced Driver Assistance Systems (ADAS) improves road safety with superior perception in low-light and harsh weather conditions compared to traditional RGB cameras. However, research in this area faces challenges due to limited dataset availability and poor representation in driving simulators. RGB-to-thermal image translation offers a potential solution, but existing methods focus on one-to-one mappings. We propose a one-to-many mapping using a multi-modal translation framework enhanced with our Component-aware Adaptive Instance Normalization (CoAdaIN). Unlike the original AdaIN, which applies styles globally, CoAdaIN adapts styles to different image components individually. The result, as we show, is more realistic and diverse thermal image translations. This is the accepted author manuscript of the paper published in IEEE Sensors Conference 2024. The final published version is available at 10.1109/SENSORS60989.2024.10785056.

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Kaili Wang, Leonardo Ravaglia, Roberto Longo, Lore Goetschalckx, David Van Hamme, Julie Moeyersoms, Ben Stoffelen, Tom De Schepper. 2025-09-27. Increasing the Diversity in RGB-to-Thermal Image Translation for Automotive Applications. https://doi.org/10.1109/sensors60989.2024.10785056

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