基于场景几何信息的显著性目标检测方法综述

doi:10.16451/j.cnki.issn1003-6059.202302003

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摘要

显著性目标检测在图像和视频压缩、伪装物体检测、医学图像分割等领域具有重要作用.随着深度传感器和光场技术的广泛应用,深度图像和光场数据等场景几何信息开始应用于显著性目标检测,可提升模型在复杂场景下的性能,由此学者们提出一系列基于场景几何信息的显著性目标检测方法.文中旨在分析总结经典的基于场景几何信息的显著性目标检测方法.首先,介绍方法的基本框架及评估标准.然后,围绕多模态特征融合、多模态信息优化、网络模型轻量化三方面,分类概述和分析经典的RGB-D显著性目标检测方法和光场显著性目标检测方法.同时,详细介绍基于场景几何信息的显著性目标检测方法的工作进展.最后,讨论方法目前存在的问题,展望未来的研究方向.

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关键词 ：显著性目标检测, 场景几何信息, RGB-D显著性目标检测, 光场显著性目标检测

Abstract：

Salient object detection is crucial in the fields of image and video compression, camouflaged object detection, medical image segmentation, etc. With the wide application of depth sensor and light field technology, scene geometric information such as depth images and light field images is applied to salient object detection to improve the performance of the model in complex scenes. Therefore, a series of salient object detection methods based on scene geometric information is proposed. The typical algorithms of salient object detection based on scene geometric information are summarized in this paper. Firstly , the basic frameworks of models and evaluation metrics are illustrated. Then, the typical algorithms of RGB-D salient object detection and light field salient object detection are summarized and analyzed from the aspects of multi-modal feature fusion, multi-modal information optimization and network model lightweight. Meanwhile, the recent progress of salient object detection methods based on scene geometry information is introduced. Finally, the problems still faced in the existing salient object detection methods based on scene geometry information are analyzed and the future research is discussed.

Key words： Salient Object Detection Scene Geometric Information RGB-D Salient Object Detection Light Field Salient Object Detection

收稿日期: 2022-08-03

ZTFLH:

TP391.41

基金资助:

国家重点研发计划项目(No.2018AAA0102001)、国家自然科学基金项目(No.62172070,62293542,U1903215)、辽宁省中央引导地方科技发展资金项目(No.2022JH6/100100028)资助

通讯作者: 朴永日,博士,副教授,主要研究方向为3D计算机视觉与感知、目标检测、目标识别、3D重建与可视化.E-mail:yrpiao@dlut.edu.cn.

作者简介: 吴岚虎,硕士研究生,主要研究方向为深度学习、显著性目标检测、3D计算机视觉与感知.E-mail:sdplwlh@mail.dlut.edu.cn.李智玮,硕士研究生,主要研究方向为深度学习、显著性目标检测、3D计算机视觉与感知.E-mail:zhiwei@mail.dlut.edu.cn.刘垒烨,硕士研究生,主要研究方向为深度学习、显著性目标检测、3D计算机视觉与感知.E-mail:flameliu@mail.dlut.edu.cn.卢湖川,博士,教授,主要研究方向为目标跟踪、显著性目标检测、图像分割.E-mail:lhchuan@dlut.edu.cn.

引用本文:

吴岚虎, 李智玮, 刘垒烨, 朴永日, 卢湖川. 基于场景几何信息的显著性目标检测方法综述[J]. 模式识别与人工智能, 2023, 36(2): 120-142. WU Lanhu, LI Zhiwei, LIU Leiye, PIAO Yongri, LU Huchuan. A Survey of Salient Object Detection Based on Scene Geometric Information. Pattern Recognition and Artificial Intelligence, 2023, 36(2): 120-142.

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