7 research outputs found
Automatic target recognition in sonar imagery using a cascade of boosted classifiers
This thesis is concerned with the problem of automating the interpretation of data representing
the underwater environment retrieved from sensors. This is an important task
which potentially allows underwater robots to become completely autonomous, keeping
humans out of harm’s way and reducing the operational time and cost of many
underwater applications. Typical applications include unexploded ordnance clearance,
ship/plane wreck hunting (e.g. Malaysia Airlines flight MH370), and oilfield inspection
(e.g. Deepwater Horizon disaster).
Two attributes of the processing are crucial if automated interpretation is to be successful.
First, computational efficiency is required to allow real-time analysis to be
performed on-board robots with limited resources. Second, detection accuracy comparable
to human experts is required in order to replace them. Approaches in the open
literature do not appear capable of achieving these requirements and this therefore has
become the objective of this thesis.
This thesis proposes a novel approach capable of recognizing targets in sonar data
extremely rapidly with a low number of false alarms. The approach was originally
developed for face detection in video, and it is applied to sonar data here for the first
time. Aside from the application, the main contribution of this thesis, therefore, is in
the way this approach is extended to reduce its training time and improve its detection
accuracy.
Results obtained on large sets of real sonar data on a variety of challenging terrains
are presented to show the discriminative power of the proposed approach. In real field
trials, the proposed approach was capable of processing sonar data real-time on-board
underwater robots. In direct comparison with human experts, the proposed approach
offers 40% reduction in the number of false alarms
Cascade of Boosted Classifiers for Rapid Detection of Underwater Objects
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.