Increasing the Diversity in RGB-to-Thermal Image Translation for Automotive Applications
This work addresses the need for improved thermal image generation in automotive safety systems, but it is incremental as it builds on existing RGB-to-thermal translation methods.
The paper tackles the problem of limited dataset availability and poor representation in driving simulators for thermal imaging in ADAS by proposing a one-to-many mapping using a multi-modal translation framework with Component-aware Adaptive Instance Normalization (CoAdaIN), resulting in more realistic and diverse thermal image translations.
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.