Domain Enriched Deep Networks for Munition Detection in Underwater 3D Sonar Imagery

Underwater sites impacted by unexploded ordnance (UXO) may pose an unacceptable risk to human and environmen-tal health. Sonar imaging is commonly used to interrogate such sites during UXO remediation, however manually identifying and classifying potential targets is difficult and time intensive. Previous work has explored training machine learning models to recognize and classify targets, however many of these 'black-box' approaches fail to model the underlying physical acoustics and require abundant training data which is often hard to obtain. Specifically, UXOs interrogated with low frequency sound often exhibit resonant behavior which re-radiates the sound after the initial scattering due to elastic and compressional properties of the object. Such effects are usually not present in clutter objects, making them advantageous in discriminating UXO from non-UXO. In this work, we propose two neural networks which specifically model resonant scattering effects in order to find UXO from a 3D synthetic aperture sonar (SAS) imaging sonar. We do this by utilizing sequence models which are efficient at modeling the spatially correlated nature of the resonant scattering features. We compare our proposal to two recent state-of-the-art algorithms on a real-world 3D SAS dataset and show superior results even when limited training data is available.