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

Tuo-zhong YAO^†, Zhi-yu XIANG^†‡, Ji-lin LIU

(Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China)
^‡ Corresponding Author
^†E-mail: thomasyao, xiangzy@zju.edu.cn
Received Mar. 18, 2008; revision accepted July 11, 2008; Crosschecked Dec. 26, 2008

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
doi:10.1631/jzus.A0820198             CLC number: TP317.4

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