Edge Enhancing Network for Salient Object Detection

Abstract:

Aiming at the problem of blurred edges in salient object detection， this paper proposes a new method that can fully utilize edge information to enhance the confidence of edge pixels. First， the triple attention module is introduced， which uses the characteristics of the predicted saliency map to directly generate foreground， background and edge attention， and the process of generating attention weights does not add any parameters. Next， the edge prediction module is introduced， which performs supervised edge prediction in the shallowest layer of the network with the biggest feature map， and fuses the predicted edge with the saliency map to refine the edges. Finally， the model is qualitatively and is quantitatively evaluated on six commonly used public datasets， and fully compared with other models， which proves that the proposed model can achieve the best results. The method proposed in this paper has 30.28 M parameters， and can predict saliency maps at 31 frames per second on GTX 1080 Ti graphics card.