Syn2Real Domain Generalization for Underwater Mine-Like Object Detection Using Side-Scan Sonar

Abstract

Underwater mine-like object (MLO) detection with deep learning suffers from limitations due to the scarcity of real-world side-scan sonar (SSS) data. This scarcity leads to overfitting, where models perform well on training data but poorly on unseen data. In this letter, we propose a synthetic to real (Syn2Real) domain generalization approach using diffusion models to address this challenge. Synthetic data generated by DDPM and DDIM models effectively enhances the training dataset. The residual noise in the final sampled images improves the model’s ability to generalize to real-world data with inherent noise and high variation. The baseline mask-region-based convolutional neural network (RCNN) model when trained on a combination of synthetic and original SSS training datasets, exhibited approximately a 35% increase in average precision (AP) compared to being trained solely on the original training data. This significant improvement highlights the potential of Syn2Real domain generalization for underwater mine detection.