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)


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.


Robotic boats Autonomous navigation Deep learning Robot vision 



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


  1. 1.
    Dunbabin, M., Grinham, A.: Quantifying spatiotemporal greenhouse gas emissions using autonomous surface vehicles. J. Field Robot. 34(1), 151–169 (2017)CrossRefGoogle Scholar
  2. 2.
    Codiga, D.L.: A marine autonomous surface craft for long-duration, spatially explicit, multidisciplinary water column sampling in coastal and estuarine systems. J. Atmos. Oceanic Technol. 32(3), 627–641 (2015)CrossRefGoogle Scholar
  3. 3.
    Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. In: Proceedings of the IEEE (1998)Google Scholar
  4. 4.
    El-Gaaly, T., Tomaszewski, C., Valada, A., Velagapudi, P., Kannan, B., Scerri, P.: Visual obstacle avoidance for autonomous watercraft using smartphones. In: Autonomous Robots and Multirobot Systems Workshop (2013)Google Scholar
  5. 5.
    Sadhu, T., Albu, A.B., Hoeberechts, M., Wisernig, E., Wyvill, B.: Obstacle detection for image-guided surface water navigation. In: 2016 13th Conference on Computer and Robot Vision (2016)Google Scholar
  6. 6.
    Castellini, A., Manca, V.: Learning regulation functions of metabolic systems by artificial neural networks. In: Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, pp. 193–200 (2009)Google Scholar
  7. 7.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105 (2012)Google Scholar
  8. 8.
    Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks. In: CVPR, pp. 2155–2162 (2014)Google Scholar
  9. 9.
    Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)CrossRefGoogle Scholar
  10. 10.
    Giusti, A., Guzzi, J., Cirean, D.C., He, F.L., Rodrguez, J.P., Fontana, F., Faessler, M., Forster, C., Schmidhuber, J., Caro, G.D., Scaramuzza, D., Gambardella, L.M.: A machine learning approach to visual perception of forest trails for mobile robots. IEEE Robot. Autom. Lett. 1(2), 661–667 (2016)CrossRefGoogle Scholar
  11. 11.
    Chakravarty, P., Kelchtermans, K., Roussel, T., Wellens, S., Tuytelaars, T., Eycken, L.V.: CNN-based single image obstacle avoidance on a quadrotor. In: 2017 IEEE International Conference on Robotics and Automation (2017)Google Scholar
  12. 12.
    Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: MICCAI, pp. 234–241 (2015)Google Scholar
  13. 13.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015)Google Scholar
  14. 14.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  15. 15.
    Perez, L., Wang, J.: The effectiveness of data augmentation in image classification using deep learning, arXiv preprint arXiv:1712.04621 (2017)
  16. 16.
    Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980 (2014)
  17. 17.
    Caruana, R., Lawrence, S., Giles, C.L.: Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping. In: Advances in Neural Information Processing Systems, pp. 402–408 (2001)Google Scholar
  18. 18.
    Kline, T.L., Korfiatis, P., Edwards, M.E., Blais, J.D., Czerwiec, F.S., Harris, P.C., King, B.F., Torres, V.E., Erickson, B.J.: Performance of an artificial multi-observer deep neural network for fully automated segmentation of polycystic kidneys. J. Digital Imaging 30(4), 442–448 (2017)CrossRefGoogle Scholar
  19. 19.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Pennisi, A., Bloisi, D.D., Nardi, D., Giampetruzzi, A.R., Mondino, C., Facchiano, A.: Skin lesion image segmentation using delaunay triangulation for melanoma detection. Comput. Med. Imaging Graph. 52, 89–103 (2016)CrossRefGoogle Scholar

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

Personalised recommendations