# Deep Learning from Shallow Dives: Sonar Image Generation and Training for Underwater Object Detection

arXiv: https://arxiv.org/abs/1810.07990 · HTML: https://mlsanity.com/paper/1810.07990/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2018.

## Built on by (most cited first, 8 of 8)

- [Rank-based Decomposable Losses in Machine Learning: A Survey](https://mlsanity.com/paper/2207.08768/) (2022)
- [Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future](https://mlsanity.com/paper/2410.05577/) (2024)
- [STARS: Zero-shot Sim-to-Real Transfer for Segmentation of Shipwrecks in Sonar Imagery](https://mlsanity.com/paper/2310.01667/) (2023)
- [S3Simulator: A benchmarking Side Scan Sonar Simulator dataset for Underwater Image Analysis](https://mlsanity.com/paper/2408.12833/) (2024)
- [A deep neural network for multi-species fish detection using multiple acoustic cameras](https://mlsanity.com/paper/2109.10664/) (2021)
- [Synth-SONAR: Sonar Image Synthesis with Enhanced Diversity and Realism via Dual Diffusion Models and GPT Prompting](https://mlsanity.com/paper/2410.08612/) (2024)
- [Exploring the Feasibility of Affordable Sonar Technology: Object Detection in Underwater Environments Using the Ping 360](https://mlsanity.com/paper/2411.05863/) (2024)
- [Beyond Pixel Similarity: Task-Aware Evaluation of GAN-Based Synthetic Sonar Data for Robotic Perception](https://mlsanity.com/paper/2609.18100/) (2026)

## Abstract (arXiv)

Among underwater perceptual sensors, imaging sonar has been highlighted for its perceptual robustness underwater. The major challenge of imaging sonar, however, arises from the difficulty in defining visual features despite limited resolution and high noise levels. Recent developments in deep learning provide a powerful solution for computer-vision researches using optical images. Unfortunately, deep learning-based approaches are not well established for imaging sonars, mainly due to the scant data in the training phase. Unlike the abundant publically available terrestrial images, obtaining underwater images is often costly, and securing enough underwater images for training is not straightforward. To tackle this issue, this paper presents a solution to this field's lack of data by introducing a novel end-to-end image-synthesizing method in the training image preparation phase. The proposed method present image synthesizing scheme to the images captured by an underwater simulator. Our synthetic images are based on the sonar imaging models and noisy characteristics to represent the real data obtained from the sea. We validate the proposed scheme by training using a simulator and by testing the simulated images with real underwater sonar images obtained from a water tank and the sea.