Salient object detection method based on multi-scale feature-fusion guided by edge information

In this paper, an Edge-information Guided Multi-scale Feature-fusion Network (EGMFNet) is proposed to solve the problems of unclear boundary and incomplete structure of saliency map extracted by deep learning saliency target detection method based on FCN and U-shaped network architecture. EGMFNet uses Residual muti-Channel Fusion Block (RCFBlock) and uses a nested U-shaped network architecture as the backbone model. At the same time, an Edge-information Guided Global Spatial Attention Module (EGSAM) is introduced at the lower level of the network to enhance spatial features and edge features. In addition, image boundary loss is introduced into the loss function, which is used to improve the quality of saliency map and keep clearer boundaries in the learning process. Experiments on four benchmark data sets show that the F values of the proposed method are increased by 1.5%, 2.7%, 1.8% and 1.6% compared with typical methods, which verifies the effectiveness of EGMFNet network model.