Research On Salient Object Detection Algorithm Based On Deep Feature Learning

Visual saliency refers to imitating the human visual system to quickly detect the most unique visually saliency area,i.e.the salient scene,and then perceives and processes this area.Salient Object Detection(SOD)refers to detecting the objects with the most attention in salient scenes.Some studies find that the low-level features in the multi-layer multi-scale features extracted by deep networks such as VGG(Visual Geometry Group)and Res Net(Residual Neural Network)contain good spatial information to detect boundaries of salient object,while high-level features have rich semantic information to locate salient object.However,these existing methods have not fully learned and utilized the high-level features and low-level features.In this paper,deep features have been effectively learned to fully excavate the rich semantic information contained in high-level features and spatial information contained in low-level features by using attention mechanism,multi-scale and asymmetric convolution,thereby improving the performance of salient object detection.The specific research work is as follows:1)a pooling-based feature pyramid network for salient object detection is proposed.In this network,two U-shaped structures are designed to enhance the initial features of the high and low layers,the general semantic channel attention module is used to select appropriate semantic feature maps,and a pyramid pool refinement module is designed to perform multiscale extraction of the semantic information of the highest-level features.Finally,experimental analysis is carried out on five datasets including ECSSD.2)a salient object detection model based on bidirectional feature pyramid network is proposed.In this network,a two-way high-level feature pyramid structure is designed to strengthen the high-level features and a refinement module is used to refine the spatial information of the low-level features.In addition,the attention mechanism is adopted to mine the spatial information of low-level features and the semantic information of high-level features.Finally,numerical comparisons and analyses are carried out on four datasets including ECSSD.3)a salient object detection network based on a multi-scale feature pyramid grid is proposed.In this network,the feature pyramid grid structure is utilized to enhance the initial features and multi-scale operation is used to extract high-level semantic information.At the same time,asymmetric convolution module is added to high-level and low-level layers.Finally,experiments are performed on four datasets including ECSSD,and the results show that the improvement of the algorithm is effective.Figure 34;Table 20;Reference 68...