Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end.

中文翻译：

---

计算机视觉中的显着目标检测技术——一项调查


立即吸引人类视觉注意力的图像区域的检测和定位是当前计算机视觉研究的重点领域。自动识别和分割此类显着图像区域的能力对计算机视觉、计算机图形和多媒体领域的应用具有直接影响。人们设计了大量的显着目标检测（SOD）方法来有效模仿人类视觉系统检测图像中显着区域的能力。根据其特征工程机制，这些方法可以大致分为两类：传统的或基于深度学习的。在这项调查中，详细回顾了传统类别和基于深度学习类别的基于图像的 SOD 的大多数有影响力的进展。在显着目标检测中经常面临的困难的背景下，讨论了相关显着性建模趋势、关键问题、核心技术和未来研究工作的范围。针对一些大型公共数据集的各种具有挑战性的案例提供了结果。还涵盖了用于评估最先进的显着对象检测模型性能的不同指标。最后提出了 SOD 的一些未来方向。