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

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.