Obstacle detection plays an important role in unmanned surface vehicles (USV). Continuous detection from images taken onboard the vessel poses a particular challenge due to the diversity of the environment and the obstacle appearance. An obstacle may be a floating piece of wood, a scuba diver, a pier, or some other part of a shoreline. In this paper we tackle this problem by proposing a new graphical model that affords a fast and continuous obstacle image-map estimation from a single video stream captured onboard 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 runs faster than reed-time. We also present 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 compares favorably in accuracy to the related approaches, requiring a fraction of computational effort.
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