Learning How to Detect Salient Objects in Nighttime Scenes

Abstract

The detection of salient objects in nighttime scene settings is an essential research issue in computer vision. None of the known approaches can accurately anticipate salient objects in the nighttime scenes. Due to the lack of visible light, spatial visual information cannot be accurately perceived by traditional and deep network models. This paper proposed a Mountain Basin Network (MBNet) to identify salient objects for distinguishing the pixel-level saliency of low-light images. To improve the objects localizations and pixel classification performances, the proposed model incorporated a High-Low Feature Aggregation Module (HLFA) to synchronize the information from a high-level branch (named Bal-Net) and a low-level branch (called Mol-Net) to fuse the global and local context, and a Hierarchical Supervision Module (HSM) was embedded to aid in obtaining accurate salient objects, particularly the small ones. In addition, a multi-supervised integration technique was explored to optimize the structure and borders of salient objects. In the meantime, to facilitate more investigation into nighttime scenes and assessment of visual saliency models, we created a new nighttime dataset consisting of thirteen categories and a total of one thousand low-light images. Our experimental results demonstrated that the suggested MBNet model outperforms seven current state-of-the-art methods for salient object detection in nighttime scenes.