5 October 2017 Small target detection using objectness and saliency
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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.
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Naiwen Zhang, Naiwen Zhang, Yang Xiao, Yang Xiao, Zhiwen Fang, Zhiwen Fang, Jian Yang, Jian Yang, Li Wang, Li Wang, Tao Li, Tao Li, } "Small target detection using objectness and saliency", Proc. SPIE 10432, Target and Background Signatures III, 104320Q (5 October 2017); doi: 10.1117/12.2278219; https://doi.org/10.1117/12.2278219

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