Semantic Segmentation of Underwater Sonar Imagery with Deep Learning

Abstract

Majority of deep learning methods are developed for RGB imagery. However, for many applications such as detecting objects underwater other types of sensors such as sonar or radar are required. One of the most precise sensors to map the seagrass disturbance is side scan sonar. Here we developed a new deep learning framework based on dilated convolution, dense module, and inception to perform semantic segmentation for automatic extraction of potholes in underwater sonar imagery. We tested our proposed approach on a collection of underwater sonar images taken from Laguna Madre in Texas. Experimental results in comparison with the ground-truth and state-of-the-art semantic segmentation methods show the efficiency and improved accuracy of our proposed method.