Progressively Feature-Enhanced Weakly Supervised for Salient Object Detection
For the weakly supervised object detection methods,it is easy to have problems such as object structure defect and rough boundary in complex scenarios.To overcome this problem,a progressively feature-enhanced,weakly supervised network for salient object detection has been proposed.First,this model uses a full convolutional neural network(ResNet-50)as the main stem for feature extraction,which helps the model learn the intrinsic features of the salient regions.To address the problem of the incomplete structure of salient objects,a Progressive Feature-Enhanced Mechanism(PFEM)is designed,which mainly includes a dual-stream semantic enhancement module and a hierarchical adaptive feature aggregation module.By reusing the progressive feature-enhancement mechanism,richer image features can be captured.Moreover,an Edge-Guided Module(EGM)is proposed to exploit the salient object edge structure.EGM can effectively aggregate the boundary formation of features and capture high-quality salient edge maps.Finally,salient edge maps are used to generate detection results with a complete structure and clear boundaries.Experimental results on five public datasets show that,compared with the classical WSSA algorithm,the Mean Absolute Error(MAE)on the PASCAL-S dataset is reduced by 21.32％,and the F-measure is increased by 6.27％,which is better than most advanced weakly supervised significance target detection algorithms.
万方数据
维普
