Resonant Scattering-Inspired Deep Networks for Munition Detection in 3D Sonar Imagery

Underwater sites affected by unexploded ordnance (UXO) pose significant risks to both human safety and environmental well-being. Sonar imaging is commonly employed to investigate such sites and aid in UXO remediation efforts. However, manually identifying and classifying potential targets in sonar data is challenging and time-consuming. Previous research has explored the use of machine-learning models to recognize and categorize targets; however, many of these approaches lack transparency and fail to consider the underlying physical acoustics. Additionally, acquiring sufficient training data for these models can often be problematic. In this study, we present a novel approach by designing neural networks that explicitly account for the unique physics involved in the problem domain. UXOs examined using low-frequency sound frequently exhibit resonant behavior, where the sound is reradiated after initial geometric scattering, owing to the elastic and compressional properties of the objects. Moreover, such resonant effects are typically absent in clutter objects, making them advantageous in discriminating UXOs from non-UXOs. Consequently, we propose several neural network architectures that leverage these resonant effects, utilizing 3D data obtained from a synthetic aperture sonar (SAS) imaging sonar. Our first proposal incorporates a recurrent neural network to model the physics-based correlation among adjacent time/spatial slices, originating from the resonant phenomena. For our second proposal, we employ intensity imagery of orthogonal projections of the 3D data cube, which capture shape-specific resonant scattering mechanisms unique to specific types of UXOs. To evaluate the effectiveness of our methods, we compare them against recent state-of-the-art (SOTA) algorithms using a real-world 3D SAS dataset. Remarkably, even when confronted with limited training data, our approaches consistently demonstrate superior results. Our findings highlight the significant potential of incorporating physical acoustics into neural network designs for UXO detection and classification, offering improved accuracy and efficiency in underwater remediation operations.