IEEE Trans Cybern. 2016 Mar;46(3):641-54. doi: 10.1109/TCYB.2015.2412251. Epub 2015 Mar 31.
Obstacle detection plays an important role in unmanned surface vehicles (USVs). The USVs operate in a highly diverse environments in which an obstacle may be a floating piece of wood, a scuba diver, a pier, or a part of a shoreline, which presents a significant challenge to continuous detection from images taken on board. This paper addresses the problem of online detection by constrained, unsupervised segmentation. To this end, a new graphical model is proposed that affords a fast and continuous obstacle image-map estimation from a single video stream captured on board a USV. The model accounts for the semantic structure of marine environment as observed from USV by imposing weak structural constraints. A Markov random field framework is adopted and a highly efficient algorithm for simultaneous optimization of model parameters and segmentation mask estimation is derived. Our approach does not require computationally intensive extraction of texture features and comfortably runs in real time. The algorithm is tested on a new, challenging, dataset for segmentation, and obstacle detection in marine environments, which is the largest annotated dataset of its kind. Results on this dataset show that our model outperforms the related approaches, while requiring a fraction of computational effort.
障碍物检测在无人水面艇 (USV) 中起着重要作用。USV 在高度多样化的环境中运行，障碍物可能是一块漂浮的木头、潜水员、码头或海岸线的一部分，这对从船上拍摄的图像进行连续检测提出了重大挑战。本文通过受限的、无监督的分割来解决在线检测的问题。为此，提出了一种新的图形模型，该模型可以从 USV 上捕获的单个视频流中快速、连续地估计障碍物图像图。该模型通过施加弱结构约束来考虑从 USV 观察到的海洋环境的语义结构。采用马尔可夫随机场框架，并推导出一种用于同时优化模型参数和分割掩模估计的高效算法。我们的方法不需要计算密集型的纹理特征提取，并且可以舒适地实时运行。该算法在一个新的、具有挑战性的海洋环境分割和障碍物检测数据集上进行了测试，这是同类最大的注释数据集。在该数据集上的结果表明，我们的模型在需要较少计算工作量的情况下，优于相关方法。
