Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (10): 2395-2404.DOI: 10.3778/j.issn.1673-9418.2104104

• Graphics and Image • Previous Articles     Next Articles

Salient Object Detection with Feature Hybrid Enhancement and Multi-loss Fusion

LI Chunbiao, XIE Linbo+(), PENG Li   

  1. Engineering Research Center of Internet of Things Technology Applications (School of Internet of Things Engineering, Jiangnan University), Ministry of Education, Wuxi, Jiangsu 214122, China
  • Received:2021-04-20 Revised:2021-06-07 Online:2022-10-01 Published:2021-06-11
  • About author:LI Chunbiao, born in 1997, M.S. candidate. His research interests include salient object detection and deep learning.
    XIE Linbo, born in 1973, Ph.D., professor, Ph.D. supervisor, member of CAA. His reearch interests include process modeling and control, intelligent detection and system safety.
    PENG Li, born in 1967, Ph.D., professor, Ph.D. supervisor, member of CAAI and CCF. His research interests include visual Internet of things, action recognition and deep learning.
  • Supported by:
    National Key Research and Development Program of China(2018YFD0400902);National Natural Science Foundation of China(61873112)


李春标, 谢林柏+(), 彭力   

  1. 物联网技术应用教育部工程研究中心(江南大学 物联网工程学院),江苏 无锡 214122
  • 通讯作者: + E-mail:
  • 作者简介:李春标(1997—),男,山东聊城人,硕士研究生,主要研究方向为显著性目标检测、深度学习。
  • 基金资助:


To tackle the problem of missing features and poor regional consistency in existing salient object detec-tion algorithms, a salient object detection network which uses feature hybrid enhancement and multi-loss fusion based on fully convolutional neural network is proposed. The network includes a context-aware prediction module (CAPM) and a feature hybrid enhancement module (FHEM). First, the context-aware prediction module is used to extract the multi-scale feature information of the image, in which the spatial-aware module (SAM) is embedded to further extract the high-level semantic information of the image. Furthermore, the feature hybrid enhancement module is used to effectively integrate the global feature information and the detailed feature information generated by the prediction module, and the integrated feature is enhanced through embedded feature aggregation module (FAM). In addition, the multi-loss fusion method is used to supervise the network, which combines the binary cross-entropy (BCE) loss function, the structured similarity (SSIM) loss function and the proposed regional augmentation (RA) loss function. The network with the multi-loss fusion method can maintain the integrity of the foreground region and enhance the regional pixel consistency. The algorithm is verified on five image datasets with multiple salient objects and complex backgrounds. Experimental results demonstrate that the algorithm effectively improves the detection accuracy of saliency objects in complex scenes, and alleviates the problem of saliency map features missing and poor regional consistency.

Key words: convolutional neural network, salient object detection, feature hybrid enhancement, regional aug-mentation loss



关键词: 卷积神经网络, 显著性目标检测, 特征混合增强, 区域增强损失

CLC Number: