Fusion Network Based on Progressive Nested Feature
SUN Junding;WANG Jinkai;TANG Chaosheng;WU Xiaosheng;School of Computer Science and Technology, Henan Polytechnic University;
In salient object detection, the computer detects the most interesting areas or objects in the visual scene by means of introducing the human visual attention mechanism. Aiming at the problems of unclear edge, incomplete object and missing detection of small objects in salient object detection, a fusion network based on progressive nested feature is proposed. Progressive compression module is adopted to continuously transfer and merge deeper features downward and make full use of advanced semantic information while the number of model parameters is reduced. A weighted feature fusion module is designed to aggregate the multi-scale features of the encoder into a feature map that can access both high-level and low-level information. Then, the aggregated features are allocated to other layers to fully obtain image context information and focus on small objects in the image. The asymmetric convolution block is introduced to further improve the detection accuracy. Experiments on six open datasets show that the proposed network achieves good detection results.
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