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A voting-based approach for fast object recognition in underwater acoustic images

机译：基于投票的水下声像快速物体识别方法

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摘要

This paper describes a voting-based approach for the fast automatic recognition of man-made objects and related attitude estimation in underwater acoustic images generated by forward-looking sonars or acoustic cameras. In general, the continuous analysis of sequences of images is a very heavy task for human operators and this is due to the poor quality of acoustic images. Hence, algorithms able to recognize an object on the basis of a priori knowledge of the model and to estimate its attitude with reference to a global coordinate system are very useful to facilitate underwater operations like object manipulation or vehicle navigation. The proposed method is capable of recognizing objects and estimating their two-dimensional attitude by using information coming from boundary segments and their angular relations. It is based on a simple voting approach directly applied to the edge discontinuities of underwater acoustic images, whose quality is usually affected by some undesired effects such as object blurring, speckle noise, and geometrical distortions degrading the edge detection. The voting approach is robust, with respect to these effects, so that good results are obtained even with images of very poor quality. The sequences of simulated and real acoustic images are presented in order to test the validity of the proposed method in terms of average estimation error and computational load.
机译：本文介绍了一种基于投票的方法，用于快速自动识别人造物体并在前瞻性声纳或声学相机生成的水下声学图像中进行相关的姿态估计。通常，对图像序列进行连续分析对于操作人员而言是一项非常繁重的任务，这是由于声学图像质量较差所致。因此，能够基于模型的先验知识识别对象并参考全局坐标系估计其姿态的算法对于促进诸如对象操纵或车辆导航的水下操作非常有用。所提出的方法能够通过使用来自边界线段及其角度关系的信息来识别物体并估计其二维姿态。它基于直接应用于水下声像边缘不连续性的简单表决方法，其质量通常受某些不良影响的影响，例如对象模糊，斑点噪声和使边缘检测降级的几何变形。就这些效果而言，表决方法是可靠的，因此即使使用质量很差的图像也可以获得良好的效果。给出了模拟和真实声像的序列，以便从平均估计误差和计算量的角度测试该方法的有效性。

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