DarkSegNet: Low-light semantic segmentation network based on image pyramid

Abstract

In the domain of computer vision, the task of semantic segmentation for images captured under low-light conditions has proven to be a formidable challenge. To address this challenge, we introduce a novel low-light semantic segmentation model named DarkSegNet. The DarkSegNet model aims to deal with the problem of semantic segmentation of low-light images. It effectively mines potential information in images by combining image pyramid decomposition, spatial low-frequency attention (SLA) module, and channel low-frequency information enhancement (CLIE) module to achieve better low-light semantic segmentation performance. These components work synergistically to effectively extract latent information embedded within the low-light image, ultimately resulting in improved performance of low-light semantic segmentation. We conduct experiments on the UAV indoor low-light LLRGBD-real dataset. Compared to other mainstream semantic segmentation methods, DarkSegNet achieves the highest mIoU of 47.9% on the UAV indoor low-light LLRGBD-real dataset. It is worth emphasizing that our model implements end-to-end training, avoiding the need to design additional image enhancement modules. The DarkSegNet network holds significant potential for facilitating drone-based rescue operations in disaster-stricken environments.