Survey of Salient Object Detection Based on Deep Learning

Abstract

With the development of deep learning, salient object detection based on deep learning has become a research hotspot in the computer vision field. Firstly, existing salient object detection methods based on deep learning are introduced from three aspects, i.e. boundary/semantic enhancement, global/local combination and auxiliary network. As the same time, the saliency maps of these methods, qualitative analysis and comparison are given. Then the main datasets and main evaluation criteria for salient object detection based on deep learning are introduced in brief. Next the performance of salient object detection methods based on deep learning are compared on some datasets, including quantitative comparison, P-R curves and visual comparison. Finally, the shortcomings of the existing methods in complex background, small objects and real-time detection are pointed out, and the future development direction of salient object detection methods based on deep learning is explored, such as complex background, real-time, small object, weakly-supervised salient object detection and so on.

Keywords

• salient object detection
• deep learning
• visual saliency