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R-Net: Recursive decoder with edge refinement network for salient object detection
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AI
DOI：10.1016/j.eswa.2024.125562.png)
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Abstract
En 中文
Fully convolutional neural (FCN) networks based on encoder-decoder structures are adopted for salient object detection (SOD) and achieve state-of-the-art performance. However, most of the previous works aggregate multilevel features layer by layer and ignore long-range dependencies of spatial context, leading to imbalanced feature processes and context separation. In addition, the blurry edges obtained from previous SOD methods inevitably influence the detection accuracy. To handle these problems, this study proposes an effective recursionbased SOD network R-Net adopting a partial cascade structure, which contains a recursive decoder module (RDM), a long-range dependency module (LRDM), and an edge refinement module (ERM). The RDM is composed of three subdecoders, which can scale different level features to the same size through recursive pooling and recursive upsampling for full-scale feature fusion. The LRDM bridges the encoder and decoder by weighting multilevel features to establish the long-range feature dependency of the spatial context. And the ERM attached to the decoder introduces the shallow reference feature to refine the blurry edges for obtaining delicate detection results. The SOD experimental results on DUTS-TE, HKU-IS, PASCAL-S, ECSSD, and DUT-OMRON datasets demonstrate that the proposed R-Net is more robust under different complex scenes compared with some stateof-the-art methods. The source codes are available at https://github.com/ZEROICEWANG/R-Net.
Keywords:
Salient object detection
Recursive decoder
Long-range dependency
Edge refinement
Journal
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7.5
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2.9W
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10.2W