Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (14): 199-206.DOI: 10.3778/j.issn.1002-8331.1906-0357

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Deep Convolution Saliency Detection Combined with Edge Feature Prior-Based Inspection

SHI Feifei, ZHANG Songlong, PENG Li   

  1. Engineering Research Center of Internet of Things Applied Technology, Ministry of Education, College of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Online:2020-07-15 Published:2020-07-14



  1. 江南大学 物联网工程学院 物联网技术应用教育部工程研究中心,江苏 无锡 214122


In view of the lack of using prior features and edge information in the current significance detection algorithm based on deep learning, it is difficult to detect the significant regions with strong robustness in complex scenes, a method for significance detection of total convolutional neural network based on edge features and prior information is proposed. Firstly, the algorithm uses three kinds of frequently used prior knowledge combined with edge information to form a priori map, and the priori map features and depth features are effectively merged through the attention mechanism. Finally, the circular convolution-feedback optimization strategy is proposed to improve the significance area iteratively, so as to produce a more reliable prediction of the final significance graph. In the end, the reliability of the algorithm is proved by experimental qualitative and quantitative analysis.

Key words: saliency detection, full convolutional network, priori information guidance, circular convolution-feedback optimization



关键词: 显著性检测, 全卷积网络, 先验信息引导, 循环卷积优化