Automatic Target Recognition for Mine Countermeasure Missions using Forward Looking Sonar data

Abstract

The detection of objects on the seafloor is a complex task. The domain of the detection and classification of naval mines is additionally complicated by the high risk nature of the task. Autonomous underwater vehicles (AUVs) have been used in naval mine countermeasures (MCM) operations to search large areas using sensors such as sidescan or synthetic aperture sonars. These sensors generally have a high coverage rate, while sacrificing spatial resolution. Conversely, sensors with higher resolution but lower coverage (such as forward-looking sonars and electro-optical cameras) are employed for the later classification and identification stages of the MCM mission. However, to autonomously execute a target reacquisition mission, it is important to be able to collect and process data automatically and, in near real time, onboard an AUV. For this purpose, an automatic target recognition (ATR) system is required. This article proposes an ATR, which can be used onboard an autonomous vehicle, capable of detecting mine-like objects in forward-looking sonar data. The ATR combines a detector and a classifier, based on convolutional neural network models, with a probabilistic grid map that filters out false positives and combines reported detections at nearby locations. A strategy, combining a survey pattern with target-mapping maneuvers automatically activated by the ATR, has been designed to maximize the performance of this ATR. The whole system has been tested in simulation as well as using data from previous MCM exercises, the results of which are presented here