Salient object detection based on Two-stream edge attention guidance

Abstract

The existing U-Net structure convolutional neural network is widely used in salient object detection, but the problem of target boundary blur is caused by the convolution pooling operation. In order to keep the edge structure of salient objects clear, a two-stream edge attention guided convolutional neural network (SEANet) method is proposed to strengthen the guided learning of edge features, mainly by guiding the edge features to compare the original image features and the depth features are enhanced to improve the accuracy of salient object detection. The loss is minimized by using consistent cross entropy loss and IOU loss to maximize the coincidence rate of the true value map and the predicted map, as well as the actual boundary and the predicted boundary. At the same time, the obtained prediction map is compared with the current mainstream nine models, and good results have been achieved. Four sets of experiments are carried out in the ablation experiment, and the experimental results also confirmed that the model has a great improvement effect on the performance of salient object detection.