Fast image-based obstacle detection from unmanned surface vehicles

TL;DR

This paper introduces a fast, real-time obstacle detection method for unmanned surface vehicles using a novel graphical model that efficiently segments obstacles from onboard video streams without intensive texture analysis.

Contribution

A new graphical model for online obstacle detection in USV imagery that is fast, efficient, and does not rely on texture features, outperforming existing methods.

Findings

Outperforms related approaches on a large marine obstacle dataset

Runs in real-time without intensive texture feature extraction

Requires significantly less computational effort

Abstract

Obstacle detection plays an important role in unmanned surface vehicles (USV). The USVs operate in 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 onboard. 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 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 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.