Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. While many models have been proposed and several applications have emerged, a deep understanding of achievements and issues remains lacking. We aim to provide a comprehensive review of recent progress in salient object detection and situate this field among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction. Covering 228 publications, we survey i) roots, key concepts, and tasks, ii) core techniques and main modeling trends, and iii) datasets and evaluation metrics for salient object detection. We also discuss open problems such as evaluation metrics and dataset bias in model performance, and suggest future research directions.

中文翻译：

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显着物体检测：调查

从自然场景中检测和分割显着物体（通常称为显着物体检测）已经引起了人们对计算机视觉的极大兴趣。虽然已经提出了许多模型并且已经出现了几种应用，但是仍然缺乏对成就和问题的深刻理解。我们的目标是全面概述显着物体检测的最新进展，并将该领域置于其他密切相关的领域，例如通用场景分割，物体提议生成和注视预测的显着性。涵盖228篇出版物，我们调查了i）根源，关键概念和任务，ii）核心技术和主要建模趋势，以及iii）用于显着物体检测的数据集和评估指标。我们还将讨论开放性问题，例如评估指标和模型性能中的数据集偏差，