FWGAN: A Weber-Law-Driven Thermal-to-Visible Translation Network for Enhanced Autonomous Driving Systems

Autonomous driving systems usually depend on RGB images but face difficulties in low-visibility conditions. Thermal infrared (TIR) imaging offers an alternative that performs well in such environments. However, TIR is hindered by low contrast, indistinct boundaries, and the absence of color, making it less compatible with standard algorithms. Recent advances in thermal infrared colorization are promising, but they face challenges such as semantic distortions, frame instability, and issues with small objects. We introduce the frame-based Weber-law-driven Generative Adversarial Network (FWGAN), a new model that converts thermal images into visible-spectrum images. FWGAN improves object recognition by maintaining textures, increasing temporal consistency, and enabling reliable detection in different scenarios. Tests on benchmark datasets like FLIR and KAIST show that FWGAN's enhancements in semantic accuracy, object detection, and visual quality significantly push forward autonomous driving technology.