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Journal of Zhejiang University SCIENCE A
ISSN 1673-565X(Print), 1862-1775(Online), Monthly
2009 Vol.10 No.6 P.786-793
Robust water hazard detection for autonomous off-road navigation
Abstract: Existing water hazard detection methods usually fail when the features of water surfaces are greatly changed by the surroundings, e.g., by a change in illumination. This paper proposes a novel algorithm to robustly detect different kinds of water hazards for autonomous navigation. Our algorithm combines traditional machine learning and image segmentation and uses only digital cameras, which are usually affordable, as the visual sensors. Active learning is used for automatically dealing with problems caused by the selection, labeling and classification of large numbers of training sets. Mean-shift based image segmentation is used to refine the final classification. Our experimental results show that our new algorithm can accurately detect not only ‘common’ water hazards, which usually have the features of both high brightness and low texture, but also ‘special’ water hazards that may have lots of ripples or low brightness.
Key words: Water hazard detection, Active learning, Adaboost, Mean-shift
References:
Open peer comments: Debate/Discuss/Question/Opinion
<1>
Teoh Chee Way@UTAR<teohcw@utar.edu.my>
2010-11-02 23:10:10
Dear Yao,
I am very interested in your work "Robust water hazard detection for autonomous off-road navigation". However, I could not access it under Springerlink as the journal is not under my subscription.
I seek your assistance to provide a copy of your work. I would like to see in details of your work.
Your help will be very much appreciated.
Thank you.
Best Regards,
Teoh Chee Way
DOI:
10.1631/jzus.A0820198
CLC number:
TP317.4
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Received:
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Revision Accepted:
2024-05-08
Crosschecked:
2008-12-26