Open AccessAutomatic Detection of the Underwater Stationary Artificial Torpedo-shaped Target Based on SAS Image
Author(s) -
Ke Li,
Xichen Wang,
Yinming Dai,
Jincong Zhou,
Yishuo Tong,
Wei Jiang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1626/1/012086
Subject(s) - artificial intelligence , computer science , computer vision , smoothing , histogram , pattern recognition (psychology) , speckle noise , underwater , noise (video) , feature (linguistics) , speckle pattern , image (mathematics) , grayscale , geology , linguistics , oceanography , philosophy
Aiming at the problems of speckle noise, seafloor background scattering interference and lack of texture feature in SAS images, an underwater torpedo-shaped artificial target detection algorithm based on Meanshift filtering and Ostu algorithm was proposed. Firstly, based on the image characteristics, the Meanshift filtering algorithm is used to confirm a appropriate threshold to achieve a smoothing effect, and eliminate the strong background speckle noise. Secondly, Ostu algorithm is used to perform binarization according to the histogram statistical information after image gray-scale. Finally, the Region growth method is used to find and eliminate the large and unrelated white areas, meanwhile, fill the holes in the target to obtain a relatively complete target contour. The experimental results show that the algorithm is simple, has a strong ability to overcome the background noise. The automatic detection of the target is accurate. The algorithm is stable with a strong engineering value.