Saliency Region Detection Based on Deconvolutional and Skip Nested Module

The end to end saliency region detection algorithms based on deep learning always had boundary information loss and contour blurring problems.Addressing these problems,this paper proposes to adopt HED model,which performs well on edge detection tasks to improve edge detection for saliency region.To detect saliency region with distinct areas and sharp edges,this paper proposes to integrate deconvolution module and skip nested architecture on the basis of HED model to construct an advanced model HED-DSN.Firstly,a deconvolution module is introduced to combine the underlying layer information with that of upper layer through multiplying pixel by pixel.Then,a skip nested architecture is employed to combine features from different levels through channel connection way.Finally,the predicted saliency map is optimized with the fully connected conditional random field.Subjective experiments are performed on the 5 most common saliency datasets,including MSRA-B,ECSSD,HKU-IS,SOD,and DUT-OMRON.The results show that the HED-DSN model performs well.Not only it can detect the salient region accurately,but also the detected region is more complete and clear.Objective qualities experiments show that the HED-DSN model is slightly higher than DSS model,one of the best model for saliency region detection,especially is nearly 0.7%higher on the SOD dataset.