A fast physics-based, environmentally adaptive underwater object detection algorithm

A new algorithm for the detection of underwater man-made objects in sonar imagery is proposed. The algorithm is made extremely fast by employing a cascaded architecture and by exploiting integral-image representations. As a result, the method makes real-time detection of objects of interest in streaming sonar data collected by an autonomous underwater vehicle feasible. No training data is required because the proposed method is adaptively tailored to the environmental characteristics of the sensed data that is collected in situ. The flexible yet rigorous approach also addresses and overcomes five major limitations that plague the most popular detection algorithms that are in common use. The power and utility of the proposed approach is demonstrated on a large, challenging data set of synthetic aperture sonar imagery collected at sea.