Budget-Aware Road Semantic Segmentation in Unseen Foggy Scenes

Abstract

In autonomous driving, the reliable and accurate identification of road-related objects plays a crucial role in ensuring safe and efficient navigation services. Unfortunately, traditional semantic segmentation methods often encounter visibility challenges, particularly in adverse weather conditions like fog, leading to an increased frequency of traffic accidents. To address this issue, we propose and evaluate two budget-aware approaches aimed at significantly improving the efficiency and accuracy of road object semantic segmentation under foggy weather conditions. The first approach involves the integration of state-of-the-art image dehazing algorithms, designed to mitigate the adverse effects of fog on input images. This method effectively enhances visibility and clarity, thereby enhancing the segmentation process. The second approach leverages advanced algorithms and models to simulate foggy environments, introducing diversity into the training dataset. By exposing the models to varying degrees of simulated fog, they become more robust and adaptive to real-world foggy conditions, ultimately leading to improved segmentation performance. To assess the effectiveness of these approaches, we employ various well-established models on publicly available datasets to accurately represent challenging foggy scenarios encountered in autonomous driving. Our experimental results demonstrate that most models exhibit noticeable accuracy improvements, with some achieving up to a 20% increase when benefiting from the two proposed solutions.