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.
维普
万方数据
