The goal of this research is to use advances in machine learning and computer vision on the ever improving
acoustic sensors for object detection and classification in underwater applications. A specific goal of the
research will be object detection and classification of mines found on the sea floor. Specifically we will
focus on two mine types from a 900 khz Marine Sonic side-scan sonar from a REMUS 100 Autonomous Underwater Vehicle.
The new and interesting element of this research will be experimentation with multiple computer vision features in
a multi feature cascasde training method. The idea is to provide a pool of
features that show promise for the application
and let the AdaBoost feature selection process choose the most valuable features. One interested new feature, which is
unique to sonar images, we call the shadow feature. Effectively it captures all information about shadows in the current
window, including width, height, etc. and more interestingly, the altitude of the vehicle and the lateral location of
the shadow in the image.
The other interesting focus of the research will be in adaptive learning techniques intended to alter the classification
algorithms as new data becomes available in order to improve results in changing environments. One technique based on
online boosting will be a batch update technique that considers all relevant information from previous training and combines
with all of the new training data. Another interesting adaptive learning consideration will involve smart EEG caps that
allow for a human in the loop to review images at high speeds and automatically determine which images were of interest to
the operator.
The target AUV for this research is the Hydroid REMUS 100. This vehicle is currently used by
the Navy for missions to find mines on the ocean floor. All of the data captured from these missions is
analyzed post mission by trained Marines. Some of the data sets for the research come from this platform.
