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Target detection based on a model of visual attention for UAV

机译:基于视觉注意模型的无人机目标检测

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Visual attention model is a kind of model with good robustness of bionic vision. For ground target detection on UAV (Unmanned Aerial Vehicle) platform, in this paper a target detection method based on visual attention model is proposed, and applied to the complex terrain background for target detection. Using amplitude modulated Fourier transform phase of Fourier transform generate a scale adaptive Gaussian filter. In order to quickly extract the ground targets in aerial images, extracted gradient feature is to detect visual saliency area, and then by using watershed transform method, target image segmentation is achieved. Experimental results show that the method in this paper is capable to be adapting to complex ground target detection, the designed adaptive Gaussian filter is not only de-noise images effectively but also can help reserve original information as possible. In addition, calculation of the proposed method is pretty simple, and so suitable for engineering application.
机译:视觉注意模型是一种具有良好仿生视觉鲁棒性的模型。针对无人机平台的地面目标检测,提出了一种基于视觉注意模型的目标检测方法,并将其应用于复杂地形背景下的目标检测。使用幅度调制的傅立叶变换,傅立叶变换的相位产生比例自适应高斯滤波器。为了快速提取航空图像中的地面目标,提取梯度特征是检测视觉显着区域,然后通过分水岭变换的方法实现目标图像的分割。实验结果表明,该方法能够适应复杂的地面目标检测,所设计的自适应高斯滤波器不仅可以有效地对图像进行降噪处理,而且可以尽可能地保留原始信息。另外,该方法的计算非常简单,因此适合工程应用。

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