为了精确地检测到舰船目标, 提出了一种基于多特征、多尺度视觉显著性的海面舰船目标检测方法。该方法首先利用多尺度自适应的顶帽算法抑制云层、油污的干扰, 然后提取双颜色空间特征以及边缘特征构成双四元数图像进行舰船显著性检测。 由于充分利用了双四元数图像, 故可对多个特征尺度进行处理, 并保证不同尺度特征之间关联性。该方法还利用人眼对不同用大小的图像关注目标不同的特点对图像进行上下采样以避免漏检和检测重叠。在得到显著图后利用自适应图像分割（OTSU）算法确定舰船所在的区域, 并在原图上标定、提取舰船目标。在多种海面情况下进行了实验分析, 结果表明: 该算法可以排除多种干扰, 精确地检测到舰船目标, 真正率达97.73%, 虚警率低至3.37%, 相较于他频域显著性检测算法在舰船检测方面有明显的优势。

To detect ship targets accurately, a new method to detect ship targets on sea surface was proposed based on multi-feature and multi-scale visual saliency. Firstly a scale-adaptive top-hat algorithm was used to suppress the interference of clouds and oil. Then, the double-quaternion images are constructed by using double-color spatial features and edge features to detect the saliency of ships. This method makes full use of the double quaternion images, so it can be operated at the same time in a number of channels, and can save operation time to guarantee the characteristics of different scale characteristics. Furthermore, the method also uses the character that the human eye focused on the different targets for image with different sized in implement of the up-down sampling to avoid the leak overlapping in image detection. When the last saliency map is obtained, the ships were segmented to ensure the target location by using the OTSU algorithm, and then the ship target was marked and extracted in the original image. The experiments were analyzed in the several sea conditions. Experimental results show that the algorithm eliminates the interference of cloud, fog and oil pollution and ship targets are detected accurately. With this algorithm, true rate iss 97.73%, and the false alarm rate as low as 3.37%. Compared to other frequency domain saliency detection algorithms in ship detection, this algorithm has obvious advantages.