Recently, real-time obstacle detection by monocular vision exhibits a promising prospect in enhancing the safety of unmanned surface vehicles (USVs). Since the obstacles that may threaten USVs generally appear below the water edge, most existing methods first detect the horizon line and then search for obstacles below the estimated horizon line. However, these methods detect horizon line only using edge or line features, which are susceptible to interference edges from clouds, waves, and land, eventually resulting in poor obstacle detection. To avoid being affected by interference edges, in this paper, we propose a novel horizon line detection method based on semantic segmentation. The method assumes a Gaussian mixture model (GMM) with spatial smoothness constraints to fit the semantic structure of marine images and simultaneously generate a water segmentation mask. The horizon line is estimated from the water boundary points via straight line fitting. Further, inspired by human visual attention mechanisms, an efficient saliency detection method based on background prior and contrast prior is presented to detect obstacles below the estimated horizon line. To reduce false positives caused by sun glitter, waves and foam, the continuity of the adjacent frames is employed to filter the detected obstacles. An extensive evaluation was conducted on a large marine image dataset collected by our ‘Jinghai VIII’ USV. The experimental results show that the proposed method significantly outperformed the recent state-of-the-art marine obstacle method by 22.07% in terms of F-score while running over 24 fps on an NVIDIA GTX1080Ti GPU.

近来，通过单眼视觉进行实时障碍物检测在增强无人水面车辆（USV）的安全性方面显示出有希望的前景。由于可能威胁USV的障碍物通常出现在水边缘以下，因此大多数现有方法会先检测出地平线，然后在估算的地平线以下寻找障碍物。然而，这些方法仅使用边缘或线特征来检测地平线，该边缘或线特征容易受到来自云，波浪和地面的干扰边缘的影响，最终导致较差的障碍物检测。为了避免受到干扰边缘的影响，本文提出了一种基于语义分割的视线检测方法。该方法假设具有空间平滑度约束的高斯混合模型（GMM）可以拟合海洋图像的语义结构，并同时生成水分割蒙版。通过直线拟合从水边界点估计地平线。此外，受人类视觉注意力机制的启发，提出了一种基于背景先验和对比先验的有效显着性检测方法，以检测估计的视线以下的障碍物。为了减少由太阳闪光，波浪和泡沫引起的误报，采用相邻帧的连续性来过滤检测到的障碍物。我们的“静海八号” USV对大型海洋图像数据集进行了广泛的评估。％在F-分数方面而运行在上的NVIDIA GTX1080Ti GPU 24帧。