MBNet: Detecting Salient Object in Low-Light Scenes
Yu Zhang, Yiyue Hu, Nan Mu · 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · 2022
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.