Research Progress of RGB-D Salient Object Detection in Deep Learning Era

被引：0
|
作者：
Cong R.-M. [1 ,2 ]
Zhang C. [1 ,2 ]
Xu M. [3 ]
Liu H.-Y. [1 ,2 ]
Zhao Y. [1 ,2 ]
机构：
[1] Institute of Information Science, Beijing Jiaotong University, Beijing
[2] Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing
[3] School of Electronic and Information Engineering, Beihang University, Beijing
来源：
Ruan Jian Xue Bao/Journal of Software | 2023年 / 34卷 / 04期
关键词：
cross-modality information interaction; depth quality perception; RGB-D images; salient object detection;
D O I：
10.13328/j.cnki.jos.006700
中图分类号：
学科分类号：
摘要：
Inspired by the human visual attention mechanism, salient object detection (SOD) aims to detect the most attractive and interesting object or region in a given scene. In recent years, with the development and popularization of depth cameras, depth map has been successfully applied to various computer vision tasks, which also provides new ideas for the salient object detection task at the same time. The introduction of depth map not only enables the computer to simulate the human visual system more comprehensively, but also provides new solutions for the detection of some difficult scenes, such as low contrast and complex backgrounds by utilizing the structure information and location information of the depth map. In view of the rapid development of RGB-D SOD task in the era of deep learning, this studyaims to sort out and summarize the existing related research outputs from the perspective of key scientific problem solutions, and conduct the quantitative analysis and qualitative comparison of different methods on the commonly used RGB-D SOD datasets. Finally, the challenges and prospects are summarized for the future development trends. © 2023 Chinese Academy of Sciences. All rights reserved.
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页码：1711 / 1731
页数：20
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