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Learning How to Detect Salient Objects in Nighttime Scenes

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Title Learning How to Detect Salient Objects in Nighttime Scenes
 
Creator Mu, Nan
Guo, Jinjia
Tang, Jinshan
 
Subject High-low feature aggregation
Hierarchical supervision
Multi-supervised integration
Nighttime images
Salient object detection
 
Description 192-201
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 lowlevel
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.
 
Date 2023-02-08T05:28:49Z
2023-02-08T05:28:49Z
2023-02
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61366
https://doi.org/10.56042/jsir.v82i2.70219
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.82(02) [February 2023]