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In the maritime sector, safe vessel navigation is of great importance,particularly in congested harbors and waterways. The focus of this work is toestimate the distance between an object of interest and potential obstaclesusing a companion UAV. The proposed approach fuses GPS data with long-rangeaerial images. First, we employ semantic segmentation DNN for discriminatingthe vessel of interest, water, and potential solid objects using raw imagedata. The network is trained with both real and images generated andautomatically labeled from a realistic AirSim simulation environment. Then, thedistances between the extracted vessel and non-water obstacle blobs arecomputed using a novel GSD estimation algorithm. To the best of our knowledge,this work is the first attempt to detect and estimate distances to unknownobjects from long-range visual data captured with conventional RGB cameras andauxiliary absolute positioning systems (e.g. GPS). The simulation resultsillustrate the accuracy and efficacy of the proposed method for visually aidednavigation of vessels assisted by UAV.

Safe Vessel Navigation Visually Aided by Autonomous Unmanned Aerial Vehicles in Congested Harbors and Waterways