Infrared salient object detection based on global guided lightweight non-local deep features

•We proposed a lightweight model (DG-Light-NLDF) for IR salient object detection.•We developed a Dilated Linear Bottleneck (DLB) module to improve the efficiency.•We designed a simplified global module to capture location information .•We constructed an IR ship image dataset (ExtIRShip) including 9,123 labelled IR images. In this paper, we studied infrared (IR) maritime salient object detection based on convolutional neural networks (CNNs). There are mainly two contributions. Firstly, we constructed a large extended IR ship image dataset (ExtIRShip) for salient maritime target detection, including 9,123 labelled IR images. Secondly, we proposed a global guided lightweight non-local depth feature (DG-Light-NLDF) model. We introduced Dilated Linear Bottleneck (DLB) to replace the standard convolution and adding a simplified global module to provide the location information of the potential salient object, the proposed method can significantly improve the efficiency of Light-NLDF. Experimental results demonstrate that the proposed DG-Light-NLDF model could detect IR maritime salient objects more accurately with less parameters. In addition, comparison experiments between two datasets validated that the larger dataset is also much more beneficial in improving saliency detection performance.