  Forward-looking sonar can capture high resolution images of underwater
scenes, but their interpretation is complex. Generic object detection in such
images has not been solved, specially in cases of small and unknown objects. In
comparison, detection proposal algorithms have produced top performing object
detectors in real-world color images. In this work we develop a Convolutional
Neural Network that can reliably score objectness of image windows in
forward-looking sonar images and by thresholding objectness, we generate
detection proposals. In our dataset of marine garbage objects, we obtain 94%
recall, generating around 60 proposals per image. The biggest strength of our
method is that it can generalize to previously unseen objects. We show this by
detecting chain links, walls and a wrench without previous training in such
objects. We strongly believe our method can be used for class-independent
object detection, with many real-world applications such as chain following and
mine detection.
