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Deep Learning Waterline Detection for Low-Cost Autonomous Boats

  • Lorenzo Steccanella
  • Domenico Bloisi
  • Jason Blum
  • Alessandro FarinelliEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 867)

Abstract

Waterline detection in images captured from a moving camera mounted on an autonomous boat is a complex task, due the presence of reflections, illumination changes, camera jitter, and waves. The pose of the boat and the presence of obstacles in front of it can be inferred by extracting the waterline. In this work, we present a supervised method for waterline detection, which can be used for low-cost autonomous boats. The method is based on a Fully Convolutional Neural Network for obtaining a pixel-wise image segmentation. Experiments have been carried out on a publicly available data set of images and videos, containing data coming from a challenging scenario where multiple floating obstacles are present (buoys, sailing and motor boats). Quantitative results show the effectiveness of the proposed approach, with 0.97 accuracy at a speed of 9 fps.

Keywords

Robotic boats Autonomous navigation Deep learning Robot vision 

Notes

Acknowledgment

This work is partially funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689341+.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lorenzo Steccanella
    • 1
  • Domenico Bloisi
    • 1
  • Jason Blum
    • 1
  • Alessandro Farinelli
    • 1
    Email author
  1. 1.Department of Computer ScienceUniversity of VeronaVeronaItaly

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