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.
Underwater chain cleaning and inspection tasks are costly and time consuming operations that must be performed periodically to guarantee the safety of the moorings. We propose a framework towards an efficient and cost-effective solution by using an autonomous underwater vehicle equipped with a forward-looking sonar. As a first step, we tackle the problem of individual chain link detection from the challenging forward-looking sonar data. To cope with occlusions and intensity variations due to viewpoint changes, the recognition problem is addressed as local pattern matching of the different link parts. We exploit the high frame-rate of the sonar to improve, by registration, the signal-to-noise ratio of the individual sonar frames and to cluster the local detections over time to increase robustness. Experiments with sonar images of a real chain are reported, showing a high percentage of correct link detections with good accuracy while potentially keeping real-time capabilities.