Ship segmentation is an important task in maritime surveillance systems. A great deal of research on image segmentation has been done in the past few years, but there appears to be some problems when directly utilizing them for ship segmentation under complex maritime background. The interference factors decreasing segmentation performance usually are from the peculiarity of complex maritime background, such as the existence of sea fog, large wakes and large waves. To deal with these interference factors, this paper presents an integrated ship segmentation method based on discriminator and extractor (ISDE). Different from traditional segmentation methods, our method consists of two components in light of the structure: Interference Factor Discriminator (IFD) and Ship Extractor (SE). SqueezeNet is employed for the implementation of IFD as the first step to make a judgment on what interference factors are contained in the input image. While DeepLabv3 + and improved DeepLabv3 + are employed for the implementation of SE as the second step to finally extract ships. We collect a ship segmentation dataset and conduct intensive experiments on it. The experimental results demonstrate that our method for ship segmentation outperforms state-of-the-art methods in terms of segmentation accuracy, especially for the images contain sea fog. Besides our method can run in real time as well.

船舶分割是海上监视系统中的重要任务。在过去的几年中已经进行了大量的图像分割研究，但是在复杂的海洋背景下直接将它们用于船舶分割时似乎存在一些问题。降低分割性能的干扰因素通常来自复杂的海事背景的特殊性，例如海雾的存在，大苏醒和大浪。针对这些干扰因素，本文提出了一种基于鉴别器和提取器（ISDE）的集成船舶分割方法。与传统的分割方法不同，根据结构，我们的方法由两个部分组成：干扰因子鉴别器（IFD）和船舶提取器（SE）。SqueezeNet被用作实现IFD的第一步，以判断输入图像中包含哪些干扰因素。虽然DeepLabv3 +和改进的DeepLabv3 +被用于SE的实现，这是最终提取船只的第二步。我们收集船舶分割数据集并对其进行深入的实验。实验结果表明，我们的船舶分割方法在分割精度方面优于最新方法，尤其是对于包含海雾的图像。此外，我们的方法也可以实时运行。我们收集船舶分割数据集并对其进行深入的实验。实验结果表明，我们的船舶分割方法在分割精度方面优于最新方法，尤其是对于包含海雾的图像。此外，我们的方法也可以实时运行。我们收集船舶分割数据集并对其进行深入的实验。实验结果表明，我们的船舶分割方法在分割精度方面优于最新方法，尤其是对于包含海雾的图像。此外，我们的方法也可以实时运行。