Evaluation of an object detection system in the submarine environment

Abstract

The object detection in underwater environment requires a perfect description of the image with appropriate features, in order to extract the right object of interest. In this paper we adopt a novel underwater object detection algorithm based on multi-scale covariance descriptor (MSCOV) for the image description and feature extraction, and support vector machine classifier (SVM) for the data classification. This approach is evaluated in pipe detection application using MARIS dataset. The result of this algorithm outperforms existing detection system using the same dataset. Computer vision in underwater environment suffers from absorption and scattering of light in water. Despite the work carried out so far, image preprocessing is the only solution to cope with this problem. This step creates a waste of time and requires hardware and software resources. But the proposed method does not require pretreatment so it accelerate the process.