MBNet: Detecting Salient Object in Low-Light Scenes

Abstract

Benefiting from the powerful features created by deep learning techniques, salient object detection has recently made significant progress. Compared with the task of detecting salient object in well-light images, the detection of salient object in low-light scenes requires not only acquiring the spatial visual saliency of images under low light conditions, but also accurately identifying the multi-scale objects of interest. Mountain Basin Network (MBNet) is proposed for salient object detection to discriminate the pixel-level saliency of low-light images. To further refine the object localization and pixel classification performance, the proposed model integrates a high-low feature aggregation module (HLFA) to synergize the information from a high level branch (named Bal-Net) and a low level branch (named Mol-Net) to fuse the global and local context, and the hierarchical supervision modules (HSM) is embedded to assist in obtaining accurate salient objects, especially the small ones. Furthermore, multi-supervised integration strategy is leveraged to optimize the structure and boundaries of salient objects. Meanwhile, to facilitate further research and evaluation of the visual saliency models, we construct a new low-light dataset, which includes 13 categories with a total of 1000 low-light images. The experimental results show that the proposed model has state-of-the-art low-light saliency detection performance compared with seven existing methods.