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《Journal of Applied Acoustics》 2020-03
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A fast weak supervised detection method of small objects in sonar imagery

XU Ligang;ZHU Keqing;WEI Linzhe;WANG Peng;The Navy's Wuxi Military Representative Office;Institute of Acoustics, Chinese Academy of Sciences;University of Chinese Academy of Sciences;  
Detection of small objects is one of the most attractive and challenging tasks in the comprehension of sonar imagery. In the paper, a fast detection method is presented under a framework of discrete cosine transform(DCT) and K-nearest neighbor(KNN) clustering. DCT is used in the generation of image fingerprint,which contributes a certain spectral sparseness to the original image; and the modified KNN model provides efficiency with a relatively low demand of labeled data. It is shown in a series of experiments that the method we proposed can reach a compromise of precision and recall rate, and achieve considerably reliable detection result on synthetic aperture sonar(SAS) images in real time imaging.
【Fund】: 中国科学院青年创新促进会项目
【CateGory Index】: TP391.41;U666.7
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