A Study on the Effect of Commonly Used Data Augmentation Techniques on Sonar Image Artifact Detection Using Deep Neural Networks

Abstract

This paper presents an empirical study that evaluates the impact of different types of augmentations on the performance of Deep Learning (DL) models for detecting imaging artifacts in Synthetic Aperture Sonar (SAS) imagery. Despite the popularity of using DL in the SAS community, the impact of augmentations that violate the geometry and physics of SAS has not been fully explored. To address this gap, we developed a unique dataset for detecting imaging artifacts in SAS imagery with DL and trained a Bayesian neural network with a ResNet architecture using widely used augmentations in DL for computer vision, as well as common augmentations used in the SAS literature. The study shows that augmentations that violate the geometry and imaging physics of SAS can negatively impact supervised classification, but can sometimes improve performance. Overall, the study provides important insights into the impact of different types of augmentations on the performance of DL models in SAS applications.