Detecting novel objects without class information is not trivial, as it is difficult to generalize from a small training set. This is an interesting problem for underwater robotics, as modeling marine objects is inherently more difficult in sonar images, and training data might not be available apriori. Detection proposals algorithms can be used for this purpose but usually requires a large amount of output bounding boxes. In this paper we propose the use of a fully convolutional neural network that regresses an objectness value directly from a Forward-Looking sonar image. By ranking objectness, we can produce high recall (96 ) with only 100 proposals per image. In comparison, EdgeBoxes requires 5000 proposals to achieve a slightly better recall of 97 , while Selective Search requires 2000 proposals to achieve 95 recall. We also show that our method outperforms a template matching baseline by a considerable margin, and is able to generalize to completely new objects. We expect that this kind of technique can be used in the field to find lost objects under the sea.
Detection of underwater objects is a critical task for a variety of underwater applications (off-shore, archeology, marine science, mine detection). This task is traditionally carried out by a skilled human operator. However, with the appearance of Autonomous Underwater Vehicles, automated processing is now needed to tackle the large amount of data produced and to enable on the fly adaptation of the missions and near real time update of the operator. In this paper we propose a new method for object detection in sonar imagery capable of processing images extremely rapidly based on the Viola and Jones boosted classifiers cascade. Unlike most previously proposed approaches based on a model of the target, our method is based on in-situ learning of the target responses and of the local clutter. Learning the clutter is vitally important in complex terrains to obtain low false alarm rates while achieving high detection accuracy. Results obtained on real and synthetic images on a variety of challenging terrains are presented to show the discriminative power of such an approach.