SCDF: Seeing Clearly Through Dark and Fog, an Adaptive Semantic Segmentation Scheme for Autonomous Vehicle

Semantic segmentation is a pivotal research area in the advancement of autonomous driving, with a particular focus on addressing adverse weather conditions such as night, rain, and fog. However, a challenging problem that remains is effectively segmenting environments that contain multiple adverse factors within the same scene. To tackle this issue, we present SCDF, an adaptive semantic segmentation approach. SCDF employs two separate image processing modules for denoising and enhancement of low-quality inputs. To enhance the denoising effect, we also optimized the loss function of its defogging module. Furthermore, we proposed a novel weight adaptation module, which enables SCDF to switch between different driving scenarios. To evaluate the effectiveness of SCDF, we conduct experiments on the ACDC and Rainy & Foggy cityscapes datasets, respectively achieving a higher mean Intersection over Union (mIoU) score of 61.4%(ACDC) and 79.1%(Rainy & Foggy Cityscapes). This promising results demonstrate the potential of SCDF in significantly improving the performance of autonomous driving systems in complex environments with multiple adverse factors.