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Fast infrared sea ship target detection based on improved BING algorithm

机译:基于改进BING算法的红外快速海船目标检测

摘要

The key to the target detection of infrared sea ship is real-time, fast and efficient detection of the real goal. The BING method, which introduces the binarization approximation calculation, can quickly detect target. But for infrared images, the approximate calculation also brings some shortcomings. The approximation of the gradient feature makes the overall gradient of the image amplitude decrease and some of the smaller gradient edge details disappear, so that leading to the weak distinguished ability. Based on the BING algorithm, we propose an improved BING algorithm which can quickly extract the candidate regions of infrared ship images. In the normed gradients(NG) feature, we introduce the Laplace difference operator and use the two-level cascaded SVM to learn them. Experimental results have shown that our method is effective and rapid to extract the region of interest (ROI) of the target ship.
机译:红外海船目标检测的关键是实时,快速,高效地检测实际目标。引入二值化近似计算的BING方法可以快速检测目标。但是对于红外图像,近似计算也带来一些不足。梯度特征的近似使得图像幅度的整体梯度减小,并且一些较小的梯度边缘细节消失,从而导致较弱的分辨能力。基于BING算法,我们提出了一种改进的BING算法,可以快速提取红外舰船图像的候选区域。在范数梯度(NG)功能中,我们引入了拉普拉斯差分算子,并使用两级级联SVM来学习它们。实验结果表明,我们的方法有效且快速地提取了目标舰船的目标区域(ROI)。

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