To visually detect sea-surface targets, the objects of interest must be effectively and rapidly isolated from the background of sea-surface images. In contrast to traditional image detection methods, which employ a single visual feature, this paper proposes a significance detection algorithm based on the fusion of multi-visual features after detecting the sea-sky-lines. The gradient edges of the sea-surface images are enhanced using a Gaussian low-pass filter to eliminate the effect of the image gradients pertaining to the clouds, wave points, and illumination. The potential region and points of the sea-sky-line are identified. The sea-sky-line is fitted through polynomial iterations to obtain a sea-surface image containing the target object. The saliency subgraphs of the high and low frequency, gradient texture, luminance, and color antagonism features are fused to obtain an integrated saliency map of the sea-surface image. The saliency target area of the sea surface is segmented. The effectiveness of the proposed method was verified. The average detection rate and time for the sea-sky-line detection were 96.3% and 1.05 fps, respectively. The proposed method outperformed the existing saliency models on the marine obstacle detection dataset and Singapore maritime dataset, with mean absolute errors of 0.075 and 0.051, respectively.

中文翻译：

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通过改进的海天线检测对海面目标进行多视觉特征显着性检测

为了从视觉上检测海面目标，必须将有效对象快速有效地与海面图像的背景隔离。相对于传统的具有单一视觉特征的图像检测方法，本文提出了一种基于多视域特征融合的重要特征检测算法。使用高斯低通滤波器增强了海面图像的梯度边缘，从而消除了与云，波点和照明有关的图像梯度的影响。确定海天线的潜在区域和点。通过多项式迭代拟合海天线，以获得包含目标对象的海面图像。高频和低频，梯度纹理，亮度，融合颜色和对立特征，以获得海面图像的综合显着图。海面的显着目标区域被分割。验证了所提方法的有效性。海天线检测的平均检测率和时间分别为96.3％和1.05 fps。该方法在海洋障碍物检测数据集和新加坡海事数据集上均优于现有的显着性模型，其平均绝对误差分别为0.075和0.051。