Knowledge Optical to Sonar (KnOTS): Towards the Transfer of Knowledge of Underwater Object Detection from Optical to Forward-Looking Sonar Imagery

Abstract

We develop an approach to detect objects in forward-looking sonar (FLS) images using corresponding optical images and without the need for expert manual labeling of sonar images. Sonar sensing is more robust to disadvantageous underwater environmental conditions than optical sensing, but the scarcity of labeled sonar data leads to decreased performance of methods which rely on an abundance of training data. We aim to transfer insights from data-rich applications such as object detection in optical imaging to the data-scarce area of object detection in sonar images. Our approach involves recording of contemporaneous images from commercially available sensors viable for use aboard unmanned underwater vehicles. We collect new optical and sonar data in a shallow, clear-water environment and employ existing object detection techniques for optical images. We leverage the commonality of the sensors’ fields of view and our algorithmic processing of the sonar image to transfer knowledge of object bounding boxes to sonar images to create a dataset. Through this transfer, we enable training of a model that detects objects in unseen sonar images and does not require optical images as input at test time.