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The Scientific World Journal
Volume 2014, Article ID 171978, 11 pages
Research Article

BgCut: Automatic Ship Detection from UAV Images

1School of Computer Software, Tianjin University, Tianjin 300072, China
2Space Star Technology Co., Ltd., Beijing 100086, China

Received 30 August 2013; Accepted 10 March 2014; Published 3 April 2014

Academic Editors: J. Shu and F. Yu

Copyright © 2014 Chao Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Ship detection in static UAV aerial images is a fundamental challenge in sea target detection and precise positioning. In this paper, an improved universal background model based on Grabcut algorithm is proposed to segment foreground objects from sea automatically. First, a sea template library including images in different natural conditions is built to provide an initial template to the model. Then the background trimap is obtained by combing some templates matching with region growing algorithm. The output trimap initializes Grabcut background instead of manual intervention and the process of segmentation without iteration. The effectiveness of our proposed model is demonstrated by extensive experiments on a certain area of real UAV aerial images by an airborne Canon 5D Mark. The proposed algorithm is not only adaptive but also with good segmentation. Furthermore, the model in this paper can be well applied in the automated processing of industrial images for related researches.