Recently, spatially constrained mixture model has become the mainstream method for the task of vision‐based obstacle detection in unmanned surface vehicles (USVs), and has shown its potential of modeling the semantic structure of the marine environment. However, the expectation maximization (EM) optimization of this model is quite sensitive to initial values and easily falls into a local optimal solution in the presence of significant rolling and pitching in rough seas. In addition, existing methods based on spatially constrained mixture model are susceptible to false positives in the presence of sun glitter. In this paper, a prior estimation network (PEN) is proposed to improve the mixture model, which together enable reliable monocular obstacle detection for USVs. We develop a weakly supervised E‐step to train the PEN for learning the semantic structure of marine images and estimating initial class priors in obstacle detection. To mitigate the influence of poor initial parameters on the convergence of EM optimization, we use the priors estimated by the PEN to calculate the initial parameters of the mixture model and automatically adjust the hyper priors on the semantic components in the mixture model. The output of the PEN is also applied to set the probability values of the outlier component in the mixture model, aiming to reduce false positives caused by sun glitter. Experimental results show that our approach outperforms the current state‐of‐the‐art monocular method by 15% improvement in sea edge estimation and a 3.3% increase in F‐score on the marine obstacle detection data set, as well as 69.5% improvement in sea edge estimation and a 39.2% increase in F‐score on our data set, while running over 40 fps.

中文翻译：

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基于先验估计网络和空间受限混合模型的无人水面车辆高效障碍物检测

最近，空间受限混合模型已成为无人水面航行器（USV）中基于视觉的障碍物检测任务的主流方法，并显示了其对海洋环境语义结构进行建模的潜力。但是，该模型的期望最大化（EM）优化对初始值非常敏感，并且在波涛汹涌的海面存在明显的起伏和俯仰的情况下，很容易陷入局部最优解。此外，基于空间受限的混合模型的现有方法在存在太阳闪光的情况下容易受到误报的影响。在本文中，提出了一种先验估计网络（PEN）来改进混合模型，该模型可共同实现USV的可靠单眼障碍物检测。我们开发了一个弱监督的E步来训练PEN以学习海洋图像的语义结构并估计障碍物检测中的初始舱先验。为了减轻不良初始参数对EM优化收敛的影响，我们使用PEN估计的先验来计算混合模型的初始参数，并自动调整混合模型中语义成分的超先验。PEN的输出还用于设置混合模型中离群成分的概率值，旨在减少由太阳闪光引起的误报。实验结果表明，我们的方法比当前最先进的单眼方法要好，其海边缘估计值提高了15％，而海角估计值提高了3.3％我们使用PEN估计的先验来计算混合模型的初始参数，并自动调整混合模型中语义成分的超级先验。PEN的输出还用于设置混合模型中离群成分的概率值，旨在减少由太阳闪光引起的误报。实验结果表明，我们的方法比当前最先进的单眼方法要好，其海边缘估计值提高了15％，而海角估计值提高了3.3％我们使用PEN估计的先验来计算混合模型的初始参数，并自动调整混合模型中语义成分的超级先验。PEN的输出还用于设置混合模型中离群成分的概率值，旨在减少由太阳闪光引起的误报。实验结果表明，我们的方法比当前最先进的单眼方法要好，其海边缘估计值提高了15％，而海角估计值提高了3.3％在以超过40 fps的速度运行时，我们的数据集上的海洋障碍物检测数据集的F分数以及海边缘估计的改进69.5％，F分数增加了39.2％。