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

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 multimodal translation framework enhanced with our Componentaware 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.