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Multi-feature aggregation network for salient object detection

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Abstract

As the network deepens, the boundary information and small object will be lost, which result in blurred edges and incomplete salient detection. In this paper, we propose a multi-feature aggregated network to solve this problem. First, the edge features are gained through the shallow network. Then, we extract multi-level salient features of the encoder layer by the multi-level features extraction module. In order to make the most of these features, we combine the edge features with the multi-level features. Finally, the fused features are sent to the residual refinement module to obtain the final saliency map. The experimental results show that the proposed method performs better than other six methods on four public datasets.

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Acknowledgements

This work is supported in part by National Natural Science Foundation of China (61976237, 61673404, 619220-72), Zhongyuan Qianren Project (ZYQR201810162), the Key Scientific Research Projects in Colleges and Universities of Henan Province (Grant Nos. 19A120014, 20A120013) and Basic research Funds of Zhongyuan University of Technology (K2020QN019).

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Authors and Affiliations

  1. School of Electronic and Information, Zhongyuan University of Technology, Zhengzhou, 450007, China

    Hu Huang, Ping Liu, Yanzhao Wang, Tongchi Zhou, Boyang Qu, Aimin Tao & Hao Zhang

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  1. Hu Huang
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  2. Ping Liu
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  3. Yanzhao Wang
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  4. Tongchi Zhou
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  5. Boyang Qu
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  6. Aimin Tao
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  7. Hao Zhang
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Correspondence to Yanzhao Wang.

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Huang, H., Liu, P., Wang, Y. et al. Multi-feature aggregation network for salient object detection. SIViP 17, 1043–1051 (2023). https://doi.org/10.1007/s11760-022-02310-3

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  • DOI: https://doi.org/10.1007/s11760-022-02310-3

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