Paper Info
Title
Waterline and obstacle detection in images from low-cost autonomous boats for environmental monitoring
Abstract
Waterline detection from images taken by cameras mounted on low-cost autonomous surface vehicles (ASVs) is a key process for obtaining a fast obstacle detection. Achieving an accurate waterline prediction is difficult due to the instability of the ASV on which the camera is mounted and the presence of reflections, illumination changes, and waves. In this work, we present a method for waterline and obstacle detection designed for low-cost ASVs employed in environmental monitoring. The proposed approach is made of two steps: (1) a pixel-wise segmentation of the current image is used to generate a binary mask separating water and non-water regions, (2) the mask is analyzed to infer the position of the waterline, which in turn is used for detecting obstacles. Experiments were carried out on two publicly available datasets containing floating obstacles such as buoys, sailing and motor boats, and swans moving near the ASV. Quantitative results show the effectiveness of the proposed approach with 98.8% pixel-wise segmentation accuracy running at 10 frames per second on an embedded GPU board.
Year
DOI
Venue
2020
10.1016/j.robot.2019.103346
Robotics and Autonomous Systems
Keywords
Field
DocType
Water detection,Autonomous surface vessels,Robotic boats,Robot vision,Water quality monitoring
Computer vision,Obstacle,Segmentation,Computer science,Artificial intelligence,Frame rate,Environmental monitoring,Binary number,Waterline
Journal
Volume
ISSN
Citations 
124
0921-8890
2
PageRank 
References 
Authors
0.45
0
4
Name
Order
Citations
PageRank
L. Steccanella120.45
D.D. Bloisi220.45
A. Castellini320.45
Alessandro Farinelli466774.16