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Deep convolutional neural networks have been widely used for saliency detection. However, most of the previous works focus on the visible light image. In this paper, there are mainly two contributions. First, we propose a new architecture named Multilevel Up-sampling Network (MLUNet) for infrared (IR) ship object saliency detection. Specifically, the architecture of MLUNet is an Encoder-Decoder like network embedded with subtraction feature filtering module (SFFM). The encoder uses the DenseNet like architecture, and the decoder part use two upsampling methods, which are deconvolution and sub-pixel convolution. SFFM is a feature subtraction module which is in charge of feature filtering. In our proposed MLUNet, SFFM is embedded after each convolution and deconvolution block. Secondly, we construct an IR ship object image dataset for saliency detection. This dataset includes 3845 IR images and ground-truth images with different backgrounds and different objects. Experimental results show that our method outperforms the state-of-the-art methods in terms of regional evaluation measures. © 2019 Association for Computing Machinery.
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