RHRSegNet: Relighting High-Resolution Night-Time Semantic Segmentation
This work addresses nighttime semantic segmentation for autonomous driving, but it appears incremental as it builds on an existing HRnet with a relighting component.
The paper tackles the problem of semantic segmentation in nighttime scenes, which is crucial for autonomous driving due to challenges like low illumination and dynamic lighting, and proposes RHRSegNet, a model that increases HRnet segmentation performance by 5% on low-light or nighttime images.
Night time semantic segmentation is a crucial task in computer vision, focusing on accurately classifying and segmenting objects in low-light conditions. Unlike daytime techniques, which often perform worse in nighttime scenes, it is essential for autonomous driving due to insufficient lighting, low illumination, dynamic lighting, shadow effects, and reduced contrast. We propose RHRSegNet, implementing a relighting model over a High-Resolution Network for semantic segmentation. RHRSegNet implements residual convolutional feature learning to handle complex lighting conditions. Our model then feeds the lightened scene feature maps into a high-resolution network for scene segmentation. The network consists of a convolutional producing feature maps with varying resolutions, achieving different levels of resolution through down-sampling and up-sampling. Large nighttime datasets are used for training and evaluation, such as NightCity, City-Scape, and Dark-Zurich datasets. Our proposed model increases the HRnet segmentation performance by 5% in low-light or nighttime images.