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.

在本文中，我们研究了基于卷积神经网络（CNN）的红外海事显着目标检测。主要有两个贡献。首先，我们构建了一个大型的扩展红外舰船图像数据集（ExtIRShip），用于显着海上目标检测，包括9,123个带标签的红外图像。其次，我们提出了一种全球制导的轻型非局部深度特征（DG-Light-NLDF）模型。我们引入了膨胀线性瓶颈（DLB）来代替标准卷积，并添加了一个简化的全局模块来提供潜在显着物体的位置信息，该方法可以显着提高Light-NLDF的效率。实验结果表明，所提出的DG-Light-NLDF模型能够以较少的参数更准确地检测红外海事显着物体。此外，