Hierarchical feature learning network for salient object detection

Abstract

How to effectively use multi-level features is the key to improve the performance of salient object detection (SOD) model. Many existing methods use the same or similar learning strategies to deal with multi-level features, while there are relatively few studies on using different learning strategies to deal with multi-level features. In addition, how to fully fuse multi-level features is an important process to obtain accurate saliency map, A novel hierarchical feature learning network (HFLNet) is proposed to realize salient object detection. The whole detection process can be divided into three stages. Firstly, ResNet-50 is used as the backbone network to extract multi-level features; Then, multi-level features are processed by residual denoising module, interaction module and adjacent pyramid module respectively; After obtaining the further learned multi-level features, a cross fusion module is proposed to progressively fuse the multi-level features from top to bottom and bottom to top, and finally fuse the results of the above two paths to obtain the final prediction results. During the training of network model, a hybrid loss function is applied to assist the training. The proposed method is compared with nine related methods on four public datasets. The experimental results show that the method not only improves the four important quantitative performance metrics, especially the MAE, but also can predict more complete saliency maps.