Efficient Salient Object Detection Model with Dilated Convolutional Networks

DOI Web Site 被引用文献1件 参考文献46件
  • GUO Fei
    Faculty of Automation and Information Engineering, Xi'an University of Technology
  • YANG Yuan
    Faculty of Automation and Information Engineering, Xi'an University of Technology
  • GAO Yong
    Faculty of Automation and Information Engineering, Xi'an University of Technology
  • YU Ningmei
    Faculty of Automation and Information Engineering, Xi'an University of Technology

書誌事項

公開日
2020-10-01
DOI
  • 10.1587/transinf.2019edp7284
公開者
一般社団法人 電子情報通信学会

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説明

<p>Introduction of Fully Convolutional Networks (FCNs) has made record progress in salient object detection models. However, in order to retain the input resolutions, deconvolutional networks with unpooling are applied on top of FCNs. This will cause the increase of the computation and network model size in segmentation task. In addition, most deep learning based methods always discard effective saliency prior knowledge completely, which are shown effective. Therefore, an efficient salient object detection method based on deep learning is proposed in our work. In this model, dilated convolutions are exploited in the networks to produce the output with high resolution without pooling and adding deconvolutional networks. In this way, the parameters and depth of the network are decreased sharply compared with the traditional FCNs. Furthermore, manifold ranking model is explored for the saliency refinement to keep the spatial consistency and contour preserving. Experimental results verify that performance of our method is superior with other state-of-art methods. Meanwhile, the proposed model occupies the less model size and fastest processing speed, which is more suitable for the wearable processing systems.</p>

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