Robust semantic segmentation method of urban scenes in snowy environment

Semantic segmentation plays a crucial role in various computer vision tasks, such as autonomous driving in urban scenes. The related researches have made significant progress. However, since most of the researches focus on how to enhance the performance of semantic segmentation models, there is a noticeable lack of attention given to the performance deterioration of these models in severe weather. To address this issue, we study the robustness of the multimodal semantic segmentation model in snowy environment, which represents a subset of severe weather conditions. The proposed method generates realistically simulated snowy environment images by combining unpaired image translation with adversarial snowflake generation, effectively misleading the segmentation model’s predictions. These generated adversarial images are then utilized for model robustness learning, enabling the model to adapt to the harshest snowy environment and enhancing its robustness to artificially adversarial perturbance to some extent. The experimental visualization results show that the proposed method can generate approximately realistic snowy environment images, and yield satisfactory visual effects for both daytime and nighttime scenes. Moreover, the experimental quantitation results generated by MFNet Dataset indicate that compared with the model without enhancement, the proposed method achieves average improvements of 4.82% and 3.95% on mAcc and mIoU, respectively. These improvements enhance the adaptability of the multimodal semantic segmentation model to snowy environments and contribute to road safety. Furthermore, the proposed method demonstrates excellent applicability, as it can be seamlessly integrated into various multimodal semantic segmentation models.