# S3Simulator: A benchmarking Side Scan Sonar Simulator dataset for Underwater Image Analysis

arXiv: https://arxiv.org/abs/2408.12833 · HTML: https://mlsanity.com/paper/2408.12833/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 23 Aug 2024.

## Builds on (most cited first, 6 of 6)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [A Comprehensive Survey of Image Augmentation Techniques for Deep Learning](https://mlsanity.com/paper/2205.01491/) (2022)
- [Machine Learning for Shipwreck Segmentation from Side Scan Sonar Imagery: Dataset and Benchmark](https://mlsanity.com/paper/2401.14546/) (2024)
- [Deep Learning from Shallow Dives: Sonar Image Generation and Training for Underwater Object Detection](https://mlsanity.com/paper/1810.07990/) (2018)
- [Synthetic Sonar Image Simulation with Various Seabed Conditions for Automatic Target Recognition](https://mlsanity.com/paper/2210.10267/) (2022)

## Built on by (most cited first, 4 of 4)

- [A Novel Context-Adaptive Fusion of Shadow and Highlight Regions for Efficient Sonar Image Classification](https://mlsanity.com/paper/2506.01445/) (2025)
- [Physics-informed simulation framework for realistic sonar image generation and statistical validation](https://mlsanity.com/paper/2605.19712/) (2026)
- [Supervised Contrastive Machine Unlearning of Background Bias in Sonar Image Classification with Fine-Grained Explainable AI](https://mlsanity.com/paper/2512.01291/) (2025)
- [BenthicDINO: Physics-Informed Self-Distillation for View-Invariant Side-Scan Sonar Representations](https://mlsanity.com/paper/2608.23215/) (2026)

## Abstract (arXiv)

Acoustic sonar imaging systems are widely used for underwater surveillance in both civilian and military sectors. However, acquiring high-quality sonar datasets for training Artificial Intelligence (AI) models confronts challenges such as limited data availability, financial constraints, and data confidentiality. To overcome these challenges, we propose a novel benchmark dataset of Simulated Side-Scan Sonar images, which we term as 'S3Simulator dataset'. Our dataset creation utilizes advanced simulation techniques to accurately replicate underwater conditions and produce diverse synthetic sonar imaging. In particular, the cutting-edge AI segmentation tool i.e. Segment Anything Model (SAM) is leveraged for optimally isolating and segmenting the object images, such as ships and planes, from real scenes. Further, advanced Computer-Aided Design tools i.e. SelfCAD and simulation software such as Gazebo are employed to create the 3D model and to optimally visualize within realistic environments, respectively. Further, a range of computational imaging techniques are employed to improve the quality of the data, enabling the AI models for the analysis of the sonar images. Extensive analyses are carried out on S3simulator as well as real sonar datasets to validate the performance of AI models for underwater object classification. Our experimental results highlight that the S3Simulator dataset will be a promising benchmark dataset for research on underwater image analysis. https://github.com/bashakamal/S3Simulator.