MFC-Net: Multi-feature fusion cross neural network for salient object detection

Zhenyu Wang, Yunzhou Zhang, Yan Liu, Shichang Liu, Sonya Coleman, Dermot Kerr

    Research output: Contribution to journalArticlepeer-review

    10 Citations (Scopus)
    89 Downloads (Pure)


    Although methods based on the fully convolutional neural networks (FCNs) have shown strong advantages in the field of salient object detection, the existing methods still have two challenging issues: insufficient multi-level feature fusion ability and boundary blur. To overcome these issues, we propose a novel salient object detection method based on a multi-feature fusion cross network (denoted MFC-Net). Firstly, to overcome the issue of insufficient multi-level feature fusion ability, inspired by the connection mode of human brain neurons, we propose a novel cross network framework, combined with contextual feature transfer modules (CFTMs) to integrate, enhance and transmit multi-level feature information in an iterative manner. Secondly, to address the issue of blurred boundaries, we effectively enhance the edge features of saliency map by a simple edge enhancement strategy. Thirdly, to reduce the loss of information caused by the saliency map generated by multi-level feature fusion, we use feature fusion modules (FFMs) to learn contextual feature information from multiple angles and then output the resulting saliency map. Finally, a hybrid loss function fully supervises the network at the pixel and object level, optimizing the network performance. The proposed MFC-Net has been evaluated using five benchmark datasets. The performance evaluation demonstrates that the proposed method outperforms other state-of-the-art methods, which proves the superiority of MFC-Net approach.

    Original languageEnglish
    Article number104243
    JournalImage and Vision Computing
    Early online date30 Jun 2021
    Publication statusPublished (in print/issue) - 1 Sept 2021

    Bibliographical note

    Funding Information:
    This work is supported by Major Science and technology innovation engineering projects of Shandong Province ( 2019JZZY010128 ), National Natural Science Foundation of China (No. 61973066 ), Open Fundation of Zhijiang Laboratory (No. 2019KD0AD01/006 ), Equipment Pre-research Fundation ( 61403120111 ), Distinguished Creative Talent Program of Liaoning Colleges and Universities ( LR2019027 ) and Fundamental Research Funds for the Central Universities ( N182608004 , N2004022 ).


    • Contextual feature transfer
    • Cross network framework
    • Feature fusion
    • Salient object detection


    Dive into the research topics of 'MFC-Net: Multi-feature fusion cross neural network for salient object detection'. Together they form a unique fingerprint.

    Cite this