DDFL: Dual-Domain Feature Learning for nighttime semantic segmentation

Abstract

Nighttime semantic segmentation has been playing a critical role in intelligent transportation, building safety and urban management. However, nighttime scenes present some challenges such as complex structures, multiple light sources, uneven lighting and blurry image noise, which severely degrade the segmentation quality of nighttime images. To address these challenges, we propose a Dual-Domain Feature Learning (DDFL) model for nighttime semantic segmentation. Our approach introduces three innovative ideas. First, we establish an exposure correction module to address the impact of lighting differences on the model’s learning, so as to maximally restore the pixel distortion and blurry areas caused by artificial light in nighttime scenes. Second, we incorporate frequency domain information into the nighttime segmentation task to give the model stronger discrimination ability. Finally, we introduce a dual-domain fusion module to complement the information of learning from the spatial and frequency domains in a cross-fusion manner, enabling the network to perceive semantic information while preserving details. The proposed model was experimentally tested on the Nightcity, Nightcity+ and BDD100k datasets. Our results demonstrate that our model outperforms mainstream models, achieving mIoU scores of 56.73%, 57.41% and 28.97%, respectively, under different lighting, image exposure levels, and resolutions. These results show that our model is capable of segmenting nighttime scenes efficiently in a high-quality way.