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Proceedings Paper

Small target detection using objectness and saliency
Author(s): Naiwen Zhang; Yang Xiao; Zhiwen Fang; Jian Yang; Li Wang; Tao Li
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Paper Abstract

We are motived by the need for generic object detection algorithm which achieves high recall for small targets in complex scenes with acceptable computational efficiency. We propose a novel object detection algorithm, which has high localization quality with acceptable computational cost. Firstly, we obtain the objectness map as in BING[1] and use NMS to get the top N points. Then, k-means algorithm is used to cluster them into K classes according to their location. We set the center points of the K classes as seed points. For each seed point, an object potential region is extracted. Finally, a fast salient object detection algorithm[2] is applied to the object potential regions to highlight objectlike pixels, and a series of efficient post-processing operations are proposed to locate the targets. Our method runs at 5 FPS on 1000*1000 images, and significantly outperforms previous methods on small targets in cluttered background.

Paper Details

Date Published: 5 October 2017
PDF: 9 pages
Proc. SPIE 10432, Target and Background Signatures III, 104320Q (5 October 2017); doi: 10.1117/12.2278219
Show Author Affiliations
Naiwen Zhang, Huazhong Univ. of Science and Technology (China)
Yang Xiao, Huazhong Univ. of Science and Technology (China)
Zhiwen Fang, Huazhong Univ. of Science and Technology (China)
Hunan Univ. of Humanities (China)
Jian Yang, Huazhong Univ. of Science and Technology (China)
Li Wang, Beijing Institute of Control Engineering (China)
Tao Li, Beijing Institute of Control Engineering (China)


Published in SPIE Proceedings Vol. 10432:
Target and Background Signatures III
Karin U. Stein; Ric Schleijpen, Editor(s)

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