A Trainable System for Underwater Pipe Detection

Abstract

Underwater image processing is widely increased over the last decade. It is a fundamental process for a most part of underwater research applications, because of the need of data acquisition. In this paper we will propose a novel approach of pipe detection in submarine environment. The system draws much of its power from a representation that describes an object class taking into account structure and content features which are computed through the multi-scale covariance descriptor. This approach describes an object detection model by training a support vector machine classifier using a large set of positive and negative samples. We present result on pipe detection using Maris dataset. Moreover, we show how the representation affects detection performance by considering mono-scale representation using Covariance descriptor.