Detection of small man-made objects in sector scan imagery using neural networks

This paper presents a neural network based system to detect small man-made objects in sequences of sector scan sonar images. The detection of such objects is considered out to ranges of 150 metres using a forward-looking sonar system mounted on a vessel. After an initial cleaning operation performed by compensating for the motion of the vessel, the imagery was segmented to extract objects for analysis. A set of 31 features extracted from each object was examined. These features consisted of basic object size and contrast features, shape moment-based features, moment invariants, and features extracted from the second-order histogram of each object. The best set of 15 features was then selected using sequential forward selection and sequential backward selection. These features were then used to train a neural network to detect man-made objects in the image sequences. The detector achieved a 97% accuracy at a mean false positive rate of 9 per frame.