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Finding mines in Sonar imagery is a significant problem with a great deal ofrelevance for seafaring military and commercial endeavors. Unfortunately, thelack of enormous Sonar image data sets has prevented automatic targetrecognition (ATR) algorithms from some of the same advances seen in othercomputer vision fields. Namely, the boom in convolutional neural nets (CNNs)which have been able to achieve incredible results - even surpassing humanactors - has not been an easily feasible route for many practitioners of SonarATR. We demonstrate the power of one avenue to incorporating CNNs into SonarATR: transfer learning. We first show how well a straightforward, flexible CNNfeature-extraction strategy can be used to obtain impressive if notstate-of-the-art results. Secondly, we propose a way to utilize the powerfultransfer learning approach towards multiple instance target detection andidentification within a provided synthetic aperture Sonar data set.

What's Mine is Yours: Pretrained CNNs for Limited Training Sonar ATR