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Small object detection algorithm for sonar image based on pixel hierarchy

机译:基于像素层次的声纳图像小目标检测算法

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In view of the unclear outline of the object characteristics and the complexity of the background noise in sonar images, an object detection algorithm is proposed based on pixel hierarchy. According to the characteristics of depth map, "depth information" is inserted into different layers of the sonar image to describe the “depth” of each layer. Due to the higher gray values of the objects, and the lower gray values of the background, the sonar image is layered according to different gray-levels. The high gray values of sonar image are divided into several areas and then the areas are marked. Using the high gray value characteristics of the object pixels in each image layers, a coefficient for characterizing the possibility of containing the object pixels in each image layer is defined so that the final object segmentation is made easier. Experimental results show that the object segmentation algorithm is accurate and fast.
机译:鉴于声纳图像中物体特征的轮廓不清晰以及背景噪声的复杂性,提出了一种基于像素层次的物体检测算法。根据深度图的特征,将“深度信息”插入声纳图像的不同层以描述每个层的“深度”。由于对象的灰度值较高,而背景的灰度值较低,因此声纳图像会根据不同的灰度等级进行分层。将声纳图像的高灰度值划分为几个区域,然后标记这些区域。利用每个图像层中的对象像素的高灰度值特性,定义了用于表征在每个图像层中包含对象像素的可能性的系数,从而使得最终的对象分割变得容易。实验结果表明,该算法是准确,快速的。

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