Multi-level Up-sampling Network for Infrared Ship Saliency Object Detection

被引:0
作者
Jiang, Tianpeng [1 ]
Liu, Zhaoying [1 ]
Li, Yujian [2 ]
机构
[1] Beijing Univ Technol, Beijing, Peoples R China
[2] Guilin Univ Elect Technol, Guilin, Peoples R China
来源
ICVIP 2019: PROCEEDINGS OF 2019 3RD INTERNATIONAL CONFERENCE ON VIDEO AND IMAGE PROCESSING | 2019年
基金
中国国家自然科学基金;
关键词
IR ship object; saliency detection; deep learning; feature filtering; Multi-level Up-sampling Network; SEGMENTATION;
D O I
10.1145/3376067.3376095
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
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 Multi-level 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.
引用
收藏
页码:63 / 68
页数:6
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