Saliency Detection Network Based on Edge Detection and Skeleton Extraction

Recently，considerable progress has been made in salient object detection based on joint multitask learning. However，false detection and leak detection persist owing to differences in optimization objectives and feature domains among different tasks. Therefore，current networks are incapable of identifying features such as saliency and object boundaries. Herein，we proposed an assisted multitask saliency detection network based on edge detection and skeleton extraction，comprising a feature extraction subnetwork，edge detection subnetwork，skeleton extraction subnetwork，and saliency filling subnetwork. The feature extraction subnetwork extracts multilevel features of images using ResNet101 pretrained model. The edge detection subnetwork selects the first three layers for feature fusion to retain the salient edge completely. The skeleton extraction subnetwork selects the last two layers for feature fusion to locate the center of the salient object accurately. Unlike the current networks，we train two subnetworks on edge detection dataset and skelecton extraction dataset to preserve the best models separately，which are used as pretrained models to assist with saliency detection tasks. Furthermore，to reduce the discrepancy between optimization objects and feature domains，the saliency filling subnetwork is designed to make the fusion and non-linear mapping for extracted edge and skeletal features. Experimental results for four datasets show that the proposed method can not only restore the missing saliency regions effectively but also outperform other methods. © 2023 Tianjin University. All rights reserved.