Improved Automatic Target Recognition in Synthetic Aperture Sonar Imagery Using Large Deep Neural Networks

A new arXiv preprint (2609.01800v1) compares modern CNN and transformer-based deep neural networks for Automatic Target Recognition (ATR) in Synthetic Aperture Sonar (SAS) imagery, finding that network size, architecture, pretraining method, and data augmentation significantly influence performance. The study aims to provide a roadmap for training state-of-the-art SAS-ATR models.

arXiv:2609.01800v1 Announce Type: new Abstract: Automatic Target Recognition ATR in Synthetic Aperture Sonar SAS is a task largely dominated by deep neural networks DNNs . Most SAS-ATR models use convolutional neural network CNN architectures whereas transformer-based architectures have had much less representation in the literature despite being state of the art in general computer vision CV research. Additionally, researchers have had mixed results in attempting to overcome challenges presented by a scarcity of labeled training data by using methods such as data augmentation and the use of pretrained weights from a variety of imaging modalities. In this work, we compare the performance of modern CNN and transformer-based DNNs to determine which architecture and training configurations elicit the highest performance in SAS-ATR. We investigate how network size, architecture, pretraining method, data augmentation and other forms of regularization affect SAS-ATR performance with a focus on producing the highest-performing model and providing a roadmap for training state-of-the-art SAS-ATR models.