Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (6): 108-116.DOI: 10.3778/j.issn.1002-8331.1912-0231

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Detection of Sea-Surface Saliency Object Based on Convolutional Neural Network

HE Yubo, LIU Kun   

  1. College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
  • Online:2021-03-15 Published:2021-03-12



  1. 上海海事大学 信息工程学院,上海 201306


The sea-surface environment is obscured to meteorological factors such as fog and the contrast of sea-surface images collected is reduced with more noise information interference, which makes it difficult to obtain the completed and accurate significance region when extracting the target significance. To solve the above problems, an improved algorithm is proposed for detecting sea-surface significance object in Deeplabv3 network. More feature information is extracted by using empty convolution and introducing global attention module. Context information of different void rates is connected by fusing the characteristic matrices. Then, the constraint term is added to the binary cross entropy loss function to constrain the significance of cloud occlusion. The model is saved after the training of the large data set and the training of the sea surface cloud shielding data set. Experimental results show that the significance region obtained by the method in this paper can describe the target region completely and the significance region changes undetermined when it is disturbed by the proposed method can describe traget region. The average F-measure value of the proposed method is 22.12%, 15.83% and 13.30% higher than that of other comparison algorithms when the occlusion degree is 30, 50 and 70.

Key words: sea-surface object, convolution neural network, saliency detecting, deep learning, Deeplabv3, global attention



关键词: 海面目标, 卷积神经网络, 显著性检测, 深度学习, Deeplabv3, 全局注意力