Two-B-real Net: Two-branch Network for Real-time Salient Object Detection

Abstract

As a hot topic in computer vision, recent researches on salient object detection (SOD) have focused on using the over-designed deep convolutional neural networks (CNNs) to improve the detection accuracy. However, these complex architectures constraint themselves to low speed and drag them on wide-ranging applications. In this paper, we simplify the over-designed networks and propose the Two-Branch Network for Real-time Salient Object Detection (Two-B-Real Net). Particularly, the Perceptual Branch and the Objectness Branch in our network can efficiently capture detailed information and distinctive objectness simultaneously. And we also design novel attention mechanisms to guide the network to focus on most saliency-related features and generate more accurate results. Extensive evaluations show that the proposed algorithm achieves the leading accuracy performance with real-time speed (125fps) which is significantly faster than the existing methods.