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Mine detection is dangerous and time-consuming: obtaining maritime situational awareness relies on manned surface platforms in or near the minefield. Navies worldwide are investing in autonomous underwater vehicles (AUVs) or Unmanned Surface Vessels (USVs) with sonar capabilities to aid in this task. The next step is to have these AUVs or USVs detect MIne-Like COntacts (MILCOs) autonomously, using deep neural networks (DNNs). Teaching DNNs to detect objects requires large amounts of good-quality data. For operational naval mines, this data is lacking because of four main reasons: (1) there are not that many mines encountered for big-data, (2) usually, only one mine is found per encounter, (3) sonar capabilities have improved over the last few years, making older sonar data less useful for training, and (4) information on current sonar capabilities and mines is classified.
We leverage a synthetic dataset of several types of mines in realistic environments to train an open-source DNN to detect MILCOs. The synthetic dataset provides many images of mines, often multiple mines per image, and an image quality similar to current sonar systems’ capabilities. We test our deep neural network on a recently published dataset of real naval mines.
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