Night-to-Day Image Translation with Road Light Attention Training for Traffic Information Detection

초록

Generative adversarial networks (GANs)-based image deep learning methods are useful to improve object visibility in nighttime driving environments, but they often fail to preserve critical road information like traffic light colors and vehicle lighting. This paper proposes a method to address this by utilizing both unpaired and four-channel paired training modules. The unpaired module performs the primary night-to-day conversion, while the paired module, enhanced with a fourth channel, focuses on preserving road details. Our key contribution is an inverse road light attention (RLA) map, which acts as this fourth channel to explicitly guide the network's learning. This map also facilitates a final cross-blending process, synthesizing the results from both modules to maximize their respective advantages. Experimental results demonstrate that our approach more accurately preserves lane markings and traffic light colors. Furthermore, quantitative analysis confirms that our method achieves superior performance across eight no-reference image quality metrics compared to existing techniques.

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